**2. IoT edge: trends and challenges**

There are four primary trends and problems in the design of IoT devices at the network's edge currently: The presence of high levels of attack vectors and security vulnerabilities necessitates the consideration of scalable security primitives early in the process, and there is a growing trend to deploy intelligence at the edge as data collection increases and even more, meaningful decisions are required. There is constant compression of an already low power envelope due to device design.

#### **2.1 Basis for connectivity and interoperability**

Myriads of smart devices might now be linked to the internet as IoT becomes more prevalent. A genuinely standard and lightweight communication stack is necessary to provide connection and compatibility among all existing heterogeneous wireless technologies. A variety of wireless technologies have already been used, causing huge communication heterogeneity and interoperability problems when developing linked IoT devices [16, 17]. The IoT infrastructure's variability makes standardization exceedingly challenging. With the presence of many strong competitors competing for the market dominance, "wars of standards" are unavoidable. In addition, no single technology is capable to provide a single solution that fully and simultaneously meets all the requirements of the IoT network, including power consumption, endpoint cost, bandwidth, connection density, latency, quality of service, operational expenses, and range. Normalization, on the other hand, is critical because it lowers barriers and promotes interoperability across different vendors and devices, permitting new goods and services to coexist with long-standing support. Guideline would be critical in the development and diffusion of IoT, since any communication stack must use methodical algorithms and lightweight protocols to save processing power and save energy [18].

The Internet, as known, links billions of devices using Internet Protocol (IP), specifically IP version 4 (IPv4) [8]. Nevertheless, because of the underlying 32-bit addressing method, IPv4 had major scaling issues, that were solved with the development of IPv6. This edition includes a distinctive 128-bit address for every connected device, as well as an updated protocol architecture to support a wide range of IoT-based heterogeneous devices [19]. concerns, numerous standards bodies, including the Institute of the Internet Engineering Task Force (IETF) and Electrical and Electronics Engineers Standards Association (IEEE-SA), have outlined a foundation for developing communication protocols and wireless technologies that will be implemented using the IoT market [6]. The IEEE 802.x family of standards was one of these organizations' most popular achievements. The IEEE 802.15.4 standard, that specifies a short-range radio frequency transmission protocol for low-power lossy (LLN) networks, low-power, low-rate, has aided in the seamless transition of wireless systems, existing wireless sensors to Internet-connected low-end devices [8]. In addition to its physical (PHY) and medium access control (MAC) layers, additional protocols (e.g., ZigBee, Thread, ISA100.11a, WirelessHART, and so on) have arisen, expanding the heterogeneity of the IoT domain. For the present, the IETF IPv6 over Low Power WPAN (6LoWPAN) working group committed to the definition of the 6LoWPAN adaption layer, that allows IPv6 datagrams to be sent across IEEE 802.15.4 networks. The collaboration of IEEE 802.15.4 compliant radios with the 6LoWPAN protocol allows for easy integration of limited devices with the Internet, which seems to be an important factor in interoperability and communication between low-end IP devices [6, 8, 18, 19].

#### **2.2 Edge intelligence**

Massive volumes of data are created, processed, communicated, saved, and analyzed when connection and internet technologies are implemented on in-vehicle devices and the IoT. According to the International Data Corporation (IDC), by 2025, the volume of data generated globally would be predominantly from the edge and would exceed 163 zettabytes (over 1000 billion gigabytes), a tenfold increase over data produced in 2016 [4, 9]. This ideal change would force designers, engineers and technology providers to reconsider how they construct new hardware solutions that go beyond the norm and cope with artificial intelligence (AI) workloads at the edge. Cloud service enterpriser have been at the front line of introducing AI to develop and improve their workloads and services over the last decade. Cloud services will be essential for the next generation of smart industries, smart cities, and smart households. Nonetheless, decreased latency requirements, growing privacy concerns, communication bandwidth constraints, and restricted power budgets have fueled the deployment of intelligence at the edge [10, 20]. Cloud-based decisions should be avoided in safety-critical applications such as autonomous driving since the time it takes to conduct a query/decision might compromise the vehicle's safety, for example, collision avoidance. As a result, local and real-time choices have to take precedence. On top of that, with the end of Moore's Law, we could never rely on the rising and heavy processing power of cloud core technologies to handle the quantity of data created by next-generation IoT systems [21]. Cloud services will be critical for doing high-level analytics, yet AI deployment at the edge is also increasingly critical. Deploying and utilizing intelligence at the edge has inherent dangers as well as a set of needs, both in terms of security and SWaP-C. In terms of security, the increasing complexity of the edge exponentially widens the security flaws, bringing up new attack routes in an infrastructure that is already striving to give increased protection. Advanced computing techniques, such as machine learning, can greatly increase the processing capabilities of wireless sensor nodes while also lowering total network power consumption through decreased wireless transmissions [22]. Pushing these tasks (data analysis and decision inference) as far as feasible would eventually optimize resource efficiency and responsiveness, leading to more autonomous and intelligent systems [23].

#### **2.3 Security**

Security in the Internet of Things era is not voluntary, and it should be a fundamental layout priority from the start and throughout the device's lifecycle. As IoT grows deep inside important enterprise infrastructures, the value of the assets

## *Future Internet of Things: Connecting the Unconnected World and Things Based on 5/6G… DOI: http://dx.doi.org/10.5772/intechopen.104673*

contained within such devices rises, making them attractive targets for attackers and hackers. Therefore, ignoring device safety as an initial design issue may endanger the whole supply chain, resulting in revenue and brand reputation risks, as well as grave life-threatening circumstances in some cases. The success of the Internet's next phase is highly reliant on the inherent trust and security of billions of linked heterogeneous network devices [6, 11, 12].

A flexible, multi-layered strategy capable of providing end-to-end security from device to cloud and everything in between is necessary to deliver a security architecture solution that completely covers an IoT platform. While the majority of the initial architectural proposals involves a three-layer design (perception, network, and application layers) [11], a common dominating choice has yet to be determined. In subsequent versions, more abstraction has been incorporated, culminating in a five-layer structure (service management, object abstraction, application layer, objects and business layer [24]. Each layer's technologies are distinct, with their own set of goals, needs, constraints, and tradeoffs. Nonetheless, the IoT's diverse set of security challenges and vulnerabilities has an inherent impact on all layers of the architecture.

Info concerning the IoT architecture was transmitted across all levels and entities, i.e., users, service providers, and devices, to ensure full compatibility between services and devices. This, however, considerably expands the entire attack surface. The four primary categories of attacks include hardware-based attacks (e.g., changing techniques or channel violent attacks), communication attacks (e.g., weak random number generators, man-in-the-middle), life cycle attacks (e.g., degradation code, oversupply at the factory) and software attacks (e.g., return oriented programming approaches, malware). Countermeasures must be implemented for each form of attack because a single weakness may split the entire device and span the whole network. A list of technologies and mitigation methods can be chosen based on the offered assets of an IoT-based product to fulfill the essential security standards that must be enforced. Meeting these standards is essential in establishing a reliable and secure IoT infrastructure that provides rigorous guarantees on security primitives. Among these security primitives are the following:


Since no security primitive by itself provides a standardized solution, it is essential to take a relevant layered approach to give the complex foundation to copiously defend the entire IoT device architecture, infrastructure, commonly known as defense in depth [30]. **Figure 1** shows a high-level overview of the various types of security solutions [31]. All of the layers lead to strengthen the safety of the IoT system, and every one addresses a distinct security issue.

• The Foundation Functions layer provides core modules that service the layers above it, such as cryptographic algorithms/engines-backed true random number generator (TRNG) modules [31]. The following cryptographic schemes stand out from the rest: (i) the Advanced Encryption Standard (AES) symmetric key

*Future Internet of Things: Connecting the Unconnected World and Things Based on 5/6G… DOI: http://dx.doi.org/10.5772/intechopen.104673*

protocol for mass information encryption, (ii) the Secure Hash Algorithm (SHA) cryptographic functions, and (iii) the Elliptical Curve Cryptography (ECC) or RSA asymmetric key algorithms for authentication and secure session key transactions. This layer offers a system that provides a special device identification, that is silicon bound, to enable various cryptographic algorithms (root key) [31]. A root key is usually held in a single-use programmable memory, which is configurated during platform manufacturing, or in physically unclonable function (PUF) mechanisms. It gives a strong method for encrypting, additional keys and data.


Non-invasive attacks are often classified into three categories: side-channel attacks, fault injection attacks, and software attacks [32]. Side-channel attacks concentrate on monitoring the system's behavior in terms of time (temporal attack), electromagnetism, and power consumption, simple power analysis (SPA), and differential power analysis (DPA), while 'it executes secure operations (e.g. cryptography) to extract the keys. The most effective technique to prevent synchronization attacks is to ensure that all operations inside a security function spend the same amount of time. Intel has solved this issue by developing a fully dedicated Advanced Encryption Standard (AES) instruction set that operates data-independent. Kocher [33] presented a platform-independent method for updating the secret key for each executive session of a cryptographic scheme, causing the synchronization patterns. Rambus [34] suggested a set of software libraries and hardware cores that are immune to secondary channel attacks such as temporal, electromagnetic, SPA, and DPA attacks. In fact, their methods are based on strategies that reduce the signal-to-noise ratio on side

channels and introduce randomization into cryptographic operations. They even implement protocol-level countermeasures, changing cryptographic protocols to include key update methods.

#### **2.4 Energy awareness**

Recent technical advancements in the information and communication technologies (ICT) industry have come at a cost, which is now associated with a 2% increase in the average carbon footprint. Nonetheless, because of the increasing of ICT scenarios and their requirements (including a massive and promising IoT ecosystem), it is predicted that by 2020, ICT improvement would be in the range of 6–8% [13]. The rapid spread of IoT technologies and their broad acceptance will require further sensory, communication, and performance add-ons, putting even more pressure on these devices' energy budgets. On the other hand, while IoT infrastructure will boost carbon footprint over the next few years, it also has the potential to be explored to minimize the environmental footprint of several major sectors of society: habitat monitoring, energy, smart cities and transportation systems (e.g., smart grid, smart traffic jam, etc.).A smart grid anchored by IoT nodes, for example, may improve total energy consumption. From a macro and "green" standpoint, IoT devices require a more efficient and sustainable use of resources, with the problem of energy consumption at the heart of any IoT system's design and development [13, 14].

IoT devices should use minimal power as possible. Because these devices require continuoual technique indefinitely, stable and reliable power sources are important enablers: repair and replacement of the battery or device are not cost-effective methods. Recent advancements in energy harvesting systems provide fundamental approaches for increasing battery life, mobility, and range [35, 36]. Furthermore, system designers must rely on existing and next-generation power management strategies (e.g., low-leak processing technologies, low-power flash memory and nonvolatile memory technologies, low-power clock and operational diagrams, protocols) to minimize the total energy budget. The effective and sustainable use of power resources is critical since energy consumption determines the life of a particular battery capacity [37], which necessitates the implementation of a set of control methods and intelligent energy management. As a general rule, Motes often function cyclically, periodically alternating phases of active and low power operation to reduce their average power consumption and hence lengthen their longevity [36]. When a device is in active operation, it often demands wireless communications, that is commonly needs the most power state of a node. In brief, as the IoT's backbone, wireless sensors would address rising energy demands and problems by introducing new energy- functional primitives.
