Abstract
The internet of things (IoT) is paving a path for connecting a plethora of smart devices together that emerges from the novel 5G-based applications. This evident heterogeneity invites the integration of diverse technologies such as wireless sensor networks (WSNs), software-defined networks (SDNs), cognitive radio networks (CRNs), delay tolerant networks (DTNs), and opportunistic networks (oppnets). However, the security and privacy are prominent conundrums due to featured compatibility and interoperability aspects of evolving directives. Blockchain is the most nascent paradigm instituted to resolve the issues of security and privacy while retaining performance standards. In this chapter, advances of blockchain technology in aforesaid networks are investigated and presented as means to be followed as security practices for pragmatically realizing the concepts.
TopBackground
Wireless Sensor Networks (WSNs) are basically ad hoc networks, amalgamating small devices embedded with sensing capabilities deployed to monitor physical activities in the surrounding area of interest. These sensor nodes should have the characteristics of large coverage area, monitoring with high precision, self-organization, random deployment and fault-tolerance, etc. Due to the possibility of providing low cost solutions, nowadays Wireless Sensor Networks (WSNs) are getting more and more attention in many real-world applications. However, the dense deployment of many sensor nodes cause unique security challenges in its management. In the meantime, adaptation of many security protocols to overcome those challenges are not straightforward because of inherent limits for energy consumption at sensor nodes as well as availability of lower memory and storage space. Therefore, it is timely important researchers to discuss the trade-off between resource consumption minimization and security maximization in WSNs.
Flexibility is the key feature of Software Defined Networks (SDNs) that elevate its standards beyond the conventional networking infrastructures (Kreutz, Ramos, & Verissimo, 2013). This concept offers advanced network management capabilities to the network administrator by enabling configuration of networking instances independent of the hardware layer. Infact, diversification exhibited in networking devices and their plethoric aggregate are contriving compatibility and interoperability debacles. In SDN, homogeneity of the both core and access networks are improved with standardizing the hardware specifications; and higher reliance on hardware based processing is transformed into an autonomous processing approach with software integration (Kumar et al., 2017). In fact, SDN envisages solutions for complex issues in traditional networking topologies and routing algorithms by integrating intelligence to the control plane. In addition, this is a paradigm shift for network operators that eases their issues with hardware layer and the ability to advance networking features with novel requirements to broadened avenues. Apart from flexibility, main benefits of SDN can be specified as: cost effectiveness (monetary), centralization, higher throughput, dynamic nature that support higher mobility, low communication latency, optimum network utilization, rapid and efficient load-balancing, fault tolerance, and adaptable/ context-aware security (Tomovic, Pejanovic-Djurisic, & Radusinovic, 2014; Jaballah, Conti, & Lal, 2019; Scott-Hayward, O'Callaghan, & Sezer, 2013).
Recently, research in the field of wireless communications has been focusing on the Fifth Generation (5G) cellular systems. Meanwhile, the 5G and beyond wireless network designers face challenging demands such as more capacity, higher data rates, lower latency, better connectivity for a massive number of users, lesser cost and energy, and more importantly improved quality of experience (QoE) (Munsub Ali, Liu, & Ejaz, 2020). Moreover, with the increase in the number of communication devices, the requirement for higher bandwidth is essential. However, with the limited and expensive radio spectrum resources, allocation of new frequency bands is an extremely difficult task. Therefore, efficient management of the spectrum under dynamic policies has recently become a prospective research topic.
Key Terms in this Chapter
Reinforcement Learning: An area of Machine learning used by software agents to maximize or determine the best possible solution based on cumulative reward in a specific environment.
Spectrum Sensing: A periodic monitoring process regarding a specific frequency band to determine whether to identify status of the presence or absence of primary users.
Software-Defined Networking: This a centralized controller that enables the management of network functions using software applications by separating the control plane by the data plane.
Crypto-Currency: This a digital currency which is used to acquire services through online. Therfore, a decentralized ledger is used to verify and execute online transactions securely.
Fog Computing: This enables computing functions at the edge of network or closer to the source of data, as a result, it allows to get rid of the necessity for cloud based storing requirement.
Consensus Mechanism: An agreement in relation to a single data value made by all members in a group of a blockchain based system such as with crypto-currencies.
Data Provenance: A piece of metadata or records that can be used to trace the validity of source data including all transition steps from the origin to the current location.