A Blockchain Definition to Clarify its Role for the Internet of Things

The term blockchain is used for disparate projects, ranging from cryptocurrencies to applications for the Internet of Things (IoT). The concept of blockchain appears therefore blurred, as the same technology cannot empower applications with extremely different requirements, levels of security and performance. This position paper elaborates on the theory of distributed systems to advance a clear definition of blockchain allowing us to clarify its possible role in the IoT. The definition binds together three elements that, as a whole, delineate those unique features that distinguish the blockchain from other distributed ledger technologies: immutability, transparency and anonymity. We note that immutability - which is imperative for securing blockchains - imposes remarkable resource consumption. Moreover, while transparency demands no confidentiality, anonymity enhances privacy but prevents user identification. As such, we raise the concern that these blockchain features clash with the requirements of most IoT applications where devices are power-constrained, data needs to be kept confidential, and users to be clearly identifiable. We consequently downplay the role of the blockchain for the IoT: this can act as a ledger external to the IoT architecture, invoked as seldom as possible and only to record the aggregate results of myriads of local (IoT) transactions that are most of the time performed off-chain to meet performance and scalability requirements

What is a Blockchain? A Definition to Clarify the Role of the Blockchain in the Internet of Things

The use of the term blockchain is documented for disparate projects, from cryptocurrencies to applications for the Internet of Things (IoT), and many more. The concept of blockchain appears therefore blurred, as it is hard to believe that the same technology can empower applications that have extremely different requirements and exhibit dissimilar performance and security. This position paper elaborates on the theory of distributed systems to advance a clear definition of blockchain that allows us to clarify its role in the IoT. This definition inextricably binds together three elements that, as a whole, provide the blockchain with those unique features that distinguish it from other distributed ledger technologies: immutability, transparency and anonimity. We note however that immutability comes at the expense of remarkable resource consumption, transparency demands no confidentiality and anonymity prevents user identification and registration. This is in stark contrast to the requirements of most IoT applications that are made up of resource constrained devices, whose data need to be kept confidential and users to be clearly known. Building on the proposed definition, we derive new guidelines for selecting the proper distributed ledger technology depending on application requirements and trust models, identifying common pitfalls leading to improper applications of the blockchain. We finally indicate a feasible role of the blockchain for the IoT: myriads of local, IoT transactions can be aggregated off-chain and then be successfully recorded on an external blockchain as a means of public accountability when required

Exact Distributed Load Centrality Computation: Algorithms, Convergence, and Applications to Distance Vector Routing

Many optimization techniques for networking protocols take advantage of topological information to improve performance. Often, the topological information at the core of these techniques is a centrality metric such as the Betweenness Centrality (BC) index. BC is, in fact, a centrality metric with many well-known successful applications documented in the literature, from resource allocation to routing. To compute BC, however, each node must run a centralized algorithm and needs to have the global topological knowledge; such requirements limit the feasibility of optimization procedures based on BC. To overcome restrictions of this kind, we present a novel distributed algorithm that requires only local information to compute an alternative similar metric, called Load Centrality (LC). We present the new algorithm together with a proof of its convergence and the analysis of its time complexity. The proposed algorithm is general enough to be integrated with any distance vector (DV) routing protocol. In support of this claim, we provide an implementation on top of Babel, a real-world DV protocol. We use this implementation in an emulation framework to show how LC can be exploited to reduce Babel's convergence time upon node failure, without increasing control overhead. As a key step towards the adoption of centrality-based optimization for routing, we study how the algorithm can be incrementally introduced in a network running a DV routing protocol. We show that even when only a small fraction of nodes participate in the protocol, the algorithm accurately ranks nodes according to their centrality

Integrating CSI Sensing in Wireless Networks: Challenges to Privacy and Countermeasures

The path toward 6G is still long and blurred, but a few key points seem to be already decided: integration of many different access networks; adoption of massive MIMO technologies; use of frequencies above current radio spectrum up to THz and beyond; and inclusion of artificial intelligence and machine learning in standard management and operations. One additional point that is less discussed, but seems key for success, is the advanced use of channel state information (CSI) for both equalization and decoding purposes as well as for sensing ones. CSI-based sensing promises a plethora of new applications and a quantum leap in service personalization and customer-centric network management. At the same time, CSI analysis, being based on the physical characteristics of the propagated signal, poses novel threats to people's privacy and security: No software-based solution or cryptographic method above the physical layer can prevent the analysis of CSI. CSI analysis can reveal people's position or activity, allow tracking them, and discover details on the environment that today can be seen only with cameras or radars. In this article, we discuss the current status of CSI-based sensing and present some technologies that can protect people's privacy and at the same time allow legitimate use of the information carried by the CSI to offer better services