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There is an urgent need to control global warming caused by humans to achieve a sustainable future. CO2 levels are rising steadily, and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015, we are still a long way to go from achieving a sustainable mode of global operation. The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption. Popular energy tracking studies (e.g., Digiconomist and the Cambridge Bitcoin Energy Consumption Index (CBECI)) have estimated energy consumption ranges from 29.96 ​TWh to 135.12 ​TWh and 26.41 ​TWh to 176.98 ​TWh, respectively for Bitcoin as of July 2021, which are equivalent to the energy consumption of countries such as Sweden and Thailand. The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO2 emission by Bitcoin as of July 2021, close to the emissions by Greece and Oman. This review compiles estimates made by various studies from 2018 to 2021. We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa. We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints. Finally, we present case studies on cryptocurrency networks, namely, Ethereum 2.0 and Pi Network, with a discussion on how they can solve some of the challenges we have identified.

Cybersecurity has benefitted from Artificial Intelligence (AI) technologies for attack detection. However, recent advances in AI techniques, in tandem with their misuse, have outpaced parallel advancements in cyberattack classification methods that have been achieved through academic and industry-led efforts. We describe the shift in the evolution of AI techniques, and we show how recent AI approaches are effective in helping an adversary attain his/her objectives appertaining to cyberattacks. We also discuss how the current architecture of computer communications enables the development of AI-based adversarial threats against heterogeneous computing platforms and infrastructures.

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

Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, we also need to communicate promptly with other entities such as hospitals so that they can make appropriate arrangements and provide timely medical information to emergency personnel on the scene including alerting those related to the injured person(s). In this paper, we have developed an intelligent protocol that uses connected and autonomous vehicles' scenarios in Intelligent Transportation System (ITS) so that prompt emergency services can be provided to reduce the death rate caused. The proposed protocol smartly connects with all the relevant entitles during the emergency while maintaining a smooth traffic flow for the arrival of the ambulance service. Moreover, our protocol also mitigates the broadcasting of messages circulating over the network for delay sensitive tasks. The evaluation results, based on the performance metrics such as channel collision, average packet delay, packet loss, and routing-overhead demonstrate that our proposed protocol outperforms previously proposed protocols such as Emergency Message Dissemination for Vehicular (EMDV), Contention Based Broadcasting (CBB), and Particle Swarm Optimization Contention-based Broadcast (PCBB) protocols. Finally, we discuss several issues and challenges that need to be addressed in the network in order to achieve more a reliable, efficient, connected, and autonomous vehicular network.

It is an irrefutable fact that the Internet of Things (IoT) will eventually change our daily lives because its applications and relevant technologies have been or will be penetrating our daily lives. Also, the IoT is aimed to connect all the things (e.g., devices and systems) together via the Internet, thus making it easy to collect the data of users or environments and to find out useful information from the gathered data by using data mining technologies. As a consequence, how intelligent systems are developed for the IoT has become a critical research topic today. This means that artificial intelligence (AI) technologies (e.g., supervised learning, unsupervised learning, and semi-supervised learning) were used in the development of intelligent systems for analyzing the data captured from IoT devices or making decisions for IoT systems. It can be easily seen that AI can make an IoT system more intelligent and thus more accurate. For example, various sensors can be used for a smart home system to pinpoint the location and analyze the behavior of a human; however, with AI technologies, a more accurate prediction can be provided on the two pieces of information of a human. One of the most important uses for AI technologies is to make IoT systems more intelligent in order to provide a more convenient environment for users; thus, how to use existing AI technologies or develop new AI technologies to construct a better IoT system has attracted the attention of researchers from different disciplines in recent years. That is why, besides using existing supervised, unsupervised, semi-supervised learning algorithms, data mining algorithms, and machine learning algorithms, several recent studies have also attempted to develop new intelligent methods for the devices or systems for the IoT. All these approaches for making an IoT system more intelligent can also be found in the articles of this Special Section.

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance. We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future.

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

The vision of the Internet of Things (IoT) covers not only the well-regulated processes of specific applications in different areas but also includes ubiquitous connectivity of more generic objects (or things and devices) in the physical world and the related information in the virtual world. For example, a typical IoT application, such as a smart city, includes smarter urban transport networks, upgraded water supply, and waste-disposal facilities, along with more efficient ways to light and heat buildings. For smart city applications and others, we require unique naming of every object and a secure, scalable, and efficient name resolution which can provide access to any object's inherent attributes with its name. Based on different motivations, many naming principles and name resolution schemes have been proposed. Some of them are based on the well-known domain name system (DNS), which is the most important infrastructure in the current Internet, while others are based on novel designing principles to evolve the Internet. Although the DNS is evolving in its functionality and performance, it was not originally designed for the IoT applications. Then, a fundamental question that arises is: can current DNS adequately provide the name service support for IoT in the future? To address this question, we analyze the strengths and challenges of DNS when it is used to support ubiquitous IoT. First, we analyze the requirements of the IoT name service by using five characteristics, namely security, mobility, infrastructure independence, localization, and efficiency, which we collectively refer to as SMILE. Then, we discuss the pros and cons of the DNS in satisfying SMILE in the context of the future evolution of the IoT environment.

In smart cities, vehicular applications require high computation capabilities and low-latency communication. Edge computing offers promising solutions for addressing these requirements because of several features, such as geo-distribution, mobility, low latency, heterogeneity, and support for real-time interactions. To employ network edges, existing fixed roadside units can be equipped with edge computing servers. Nevertheless, there are situations where additional infrastructure units are required to handle temporary high traffic loads during public events, unexpected weather conditions, or extreme traffic congestion. In such cases, the use of flying roadside units are carried by unmanned aerial vehicles (UAVs), which provide the required infrastructure for supporting traffic applications and improving the quality of service. UAVs can be dynamically deployed to act as mobile edges in accordance with traffic events and congestion conditions. The key benefits of this dynamic approach include: 1) the potential for characterizing the environmental requirements online and performing the deployment accordingly, and 2) the ability to move to another location when necessary. We propose a traffic-aware method for enabling the deployment of UAVs in vehicular environments. Simulation results show that our proposed method can achieve full network coverage under different scenarios without extra communication overhead or delay.

Healthcare data management has been gaining a lot of attention in recent years because of its high potential to provide more accurate and cost-efficient patient care. The traditional client-server and cloud-based healthcare data management systems suffer from the issues of single point of failure, data privacy, centralized data stewardship, and system vulnerability. The replication mechanism, and privacy and security features of blockchain have a promising future in the healthcare domain as they can solve some of the inherent issues of the health management system. However, most of the recent research works on blockchain in the healthcare domain have primarily focused on the permission-less Bitcoin network that suffers from drawbacks such as high energy consumption, limited scalability, and low transaction throughput. Consequently, there is a need for a scalable, fault-tolerant, secure, traceable and private blockchain to suit the requirements of the healthcare domain. We propose a lightweight blockchain architecture for the healthcare data management that reduces the computational and communication overhead compared to the Bitcoin network by dividing the network participants into clusters and maintaining one copy of the ledger per cluster. Our architecture introduces the use of canal, that allows secure and confidential transactions within a group of network participants. Furthermore, we propose a solution to avoid forking which is prevalent in the Bitcoin network. We demonstrate the effectiveness of our proposed architecture in providing security and privacy compared to the Bitcoin network by analyzing different threats and attacks. We also discuss how our proposed architecture addresses the identified threats. Our experimental results demonstrate that our proposed architecture generates 11 times lower network traffic compared to the Bitcoin network as the number of blocks increases. Our ledger update is 1.13 times faster. Our architecture shows a speedup of 67% in ledger update and 10 times lower network traffic when the number of nodes increases.

Cyber-Physical Systems (CPSs) are large-scale complex systems that monitor and control physical processes by using computer algorithms tightly integrated with networking and their users. Monitoring and controlling the physical environment is a hot topic for today's researchers and engineers in academia and industry. Within this realm, an important feature of current and future Information and Communications Technology (ICT) systems is self-adaptation-yet there is a shortage of information focusing on this characteristic in the literature, particularly as it relates to CPSs. Here, we investigate current state-of-the-art research on CPSs from this perspective, and evaluate the main self-adaptive approaches proposed in the literature, along with their results, strengths, and weaknesses. We also discuss appropriate techniques for enabling self-adaptation capabilities within CPSs at different architectural layers. Overcoming the challenges associated with designing and implementing self-adaptive mechanisms in CPSs will provide a path for bolstering a new generation of CPSs with greater robustness and reliability.

One of the most important Internet of Things applications is the wireless body sensor network (WBSN), which can provide universal health care, disease prevention, and control. Due to large deployments of small scale smart sensors in WBSNs, security, and privacy guarantees (e.g., security and safety-critical data, sensitive private information) are becoming a challenging issue because these sensor nodes communicate using an open channel, i.e., Internet. We implement data integrity (to resist against malicious tampering) using the secure hash algorithm 3 (SHA-3) when smart sensors in WBSNs communicate with each other using the Internet. Due to the limited resources (i.e., storage, computation, and communication capabilities) of sensors in WBSNs, a lightweight implementation of SHA-3 is needed. To address this challenge, we propose a new implementation of the SHA-3, which has a compact hardware architecture. Our implementation of SHA-3 consists of a reliable logic structure, random access memory, and an enhanced finite state machine. The simulation on a Vitrtex-5 field programmable gate array shows that the proposed implementation is suitable for the WBSN on different applications. We evaluate the sensor area of the proposed SHA-3 implementation and compare it with other recently proposed hardware implementations of SHA-3. In addition, our hardware implementation approach reduces the area by almost 74.7% compared with the recently proposed hardware implementation which has the smallest area.

Among current techniques for dynamic access to television (TV) white space (TVWS), geolocation database-based access provides a promising performance in protecting the TV-band incumbents from interference that cannot be efficiently achieved in other license-exempt models. However, in heterogeneous wireless networks, most portable devices do not have such access and may cause interference to TV incumbents. We propose a hierarchical model for spectrum sharing in TVWS that includes a wide range of fixed and portable devices. In the first tier, the TV broadcaster can lease the spectrum bands to local fixed users based on a soft license agreement. The fixed users are allowed to share access to this spectrum with some mobile users in their proximity in exchange for cooperative relaying. We consider a practical scenario, where only partial channel state information (CSI) is available at the users' transmitters, and we propose a robust algorithm against such uncertainties in CSI values. We also propose a reputation-based relay selection mechanism to identify selfish portable users. The proposed spectrum sharing framework can provide a practical model for TVWS-coexistence that prevents undesired interference to the incumbents while restricting interference among the unlicensed devices. The simulation results show the enhancement of fixed users' rate compared with alternative relay selection methods.

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future.

Based on the Information-Centric Networking (ICN) concept and the mature model of the current TCP/IP-based Internet, we propose content searching based on universal and scalable resource description, namely ISBORD (Internet Searching based on Resource Description). This novel concept aims to improve the efficiency of content searching and simplifies the end-user functionality to support the evolution of the content-centric Internet.

Two of the most promising candidate solutions for realizing the next-generation all-IP mobile networks are Mobile IPv6 (MIPv6), which is the host-based and global mobility supporting protocol, and Proxy MIPv6 (PMIPv6), which is the network-based and localized mobility supporting protocol. However, the unprecedented growth of mobile Internet traffic has resulted in the development of distributed mobility management (DMM) architecture by the Internet engineering task force DMM working group. The extension of the basic MIPv6 and PMIPv6 to support their distributed and scalable deployment in the future is one of the major goals of the DMM working group. We propose an all-IP-based mobility management architecture that leverages the concept of Named Data Networking (NDN), which is a distributed content management and addressing architecture. In the proposed solution, mobility support services are distributed among multiple anchor points at the edge of the network, thereby enabling a flat architecture that exploits name-based routing in NDN. Our approach overcomes some of the major limitations of centralized IP mobility management solutions, by extending existing routing protocol and mobility management architecture, to distribute the mobility management function of anchor points in the IP network and optimize the transmission path of mobile traffic.

Wireless sensor networks (WSNs) will be integrated into the future Internet as one of the components of the Internet of Things, and will become globally addressable by any entity connected to the Internet. Despite the great potential of this integration, it also brings new threats, such as the exposure of sensor nodes to attacks originating from the Internet. In this context, lightweight authentication and key agreement protocols must be in place to enable end-to-end secure communication. Recently, Amin et al. proposed a three-factor mutual authentication protocol for WSNs. However, we identified several flaws in their protocol. We found that their protocol suffers from smart card loss attack where the user identity and password can be guessed using offline brute force techniques. Moreover, the protocol suffers from known session-specific temporary information attack, which leads to the disclosure of session keys in other sessions. Furthermore, the protocol is vulnerable to tracking attack and fails to fulfill user untraceability. To address these deficiencies, we present a lightweight and secure user authentication protocol based on the Rabin cryptosystem, which has the characteristic of computational asymmetry. We conduct a formal verification of our proposed protocol using ProVerif in order to demonstrate that our scheme fulfills the required security properties. We also present a comprehensive heuristic security analysis to show that our protocol is secure against all the possible attacks and provides the desired security features. The results we obtained show that our new protocol is a secure and lightweight solution for authentication and key agreement for Internet-integrated WSNs.

Over the last few years, identity-based cryptosystem (IBC) has attracted widespread attention because it avoids the high overheads associated with public key certificate management. However, an unsolved but critical issue about IBC is how to revoke a misbehaving user. There are some revocable identity-based encryption schemes that have been proposed recently, but little work on the revocation problem of identity-based signature has been undertaken so far. One approach for revocation in identity-based settings is to update users' private keys periodically, which is usually done by the key generation center (KGC). But with this approach, the load on the KGC will increase quickly when the number of users increases. In this paper, we propose an efficient revocable identity-based signature (RIBS) scheme in which the revocation functionality is outsourced to a cloud revocation server (CRS). In our proposed approach, most of the computations needed during key-updates are offloaded to the CRS. We describe the new framework and the security model for the RIBS scheme with CRS and we prove that the proposed scheme is existentially unforgeable against adaptively chosen messages and identity attacks in the random oracle model. Furthermore, we monstrate that our scheme outperforms previous IBS schemes in terms of lower computation and communication costs.

Decision fusion is an important issue in wireless sensor networks (WSN), and intuitionistic fuzzy set (IFS) is a novel method for dealing with uncertain data. We propose a multi-attribute decision fusion model based on IFS, which includes two aspects: data distribution-based IFS construction algorithm (DDBIFCA) and the category similarity weight-based TOPSIS intuitionistic fuzzy decision algorithm (CSWBT-IFS). The DDBIFCA is an IFS construction algorithm that transforms the original attribute values into intuitionistic fuzzy measures, and theCSWBT-IFS is an intuitionistic fuzzy aggregation algorithm improved by the traditional TOPSIS algorithm, which combines intuitionistic fuzzy values of different attributes and obtains a final decision for the monitoring target. Both algorithms have benefits, such as low energy consumption and low computational complexity, which make them suitable for implementation in energy-constrained WSNs. Simulation results show the efficiency of intuitionistic fuzzification for the DDBIFCA and a high classification accuracy, compared with traditional fuzzy fusion and other intuitionistic fuzzy aggregation algorithms, for the CSWBT-IFS.

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.

Bluetooth technology started off as a wireless, short-range cable replacement technology but it has undergone significant developments over the last two decades. Bluetooth radios are currently embedded in almost all computing devices including personal computers, smart phones, smart watches, and even micro-controllers. For many of us, Bluetooth is an essential technology that we use every day. We provide an insight into the history of Bluetooth and its significant design developments over the last 25 years. We also discuss related issues (including security) and Bluetooth as a driving technology for the Internet of Things (IoT). Finally, we also present recent research results obtained with Bluetooth technology in various application areas.

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings.

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

Over the last few years, we have witnessed a growing interest in cyber physical systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, and aerospace) of the economy. CPSs have the ability to transform the way human-to-human, human-to-object, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of wireless sensor networking (WSN) technologies in many applications makes them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.

Green Networking can be the way to help reduce carbon emissions by the Information and Communications Technology (ICT) Industry. This paper presents some of the major components of Green Networking and discusses how the carbon footprint of these components can be reduced.