Charging of Electric Vehicles (EVs) from stationary roadside charging points (CPs) that are connected to a smart grid constitute as unique transaction events. These events can thus be verified by and stored over a distributed blockchain over the grid, where the smart CPs themselves act as miners. However, mining being an inherently resource intensive task, the grid is expected to remain fairly loaded due to a high EV charging density. Hence, towards smooth EV adoption, in this work, we propose to reduce relevant mining overheads for such a blockchain over smart EV-utility grid using geographic segmentation of the miner set. Depending on the physical locations of the CPs and their connectivity graph, we divide an initially unsegmented mining cluster into several smaller clusters geographically, where each cluster verifies only their local transactions. This significantly reduces important mining parameters per-transaction and per-block, while achieving parallelism of block generation across segments. We argue that for blockchain transactions like EV charging that are coupled to their geographic source locations, it is sufficient to have smaller independent, but parallel mining segments. Through a detailed analysis, we also prove that reducing the mining segment size does not necessarily compromise on the blockchain security. We corroborate our claims through extensive experimental results, where the proposed solution achieves an average improvement of 48% with respect to CPU cycles expended and mining energy consumed per block and 45% with respect to mining time per block with half-sized segments as compared to an unsegmented miner set. Our experimental results further prove that the proposed segmentation outperforms other geographic clustering such as K-Means clustering for blockchain applications. Additionally, owing to parallel block generation, we show that by segmentation, the mining energy required per block is reduced from 19 J to about 0.02 J over the same grid, providing a significant energy efficiency.

This work proposes routeNow, a routing algorithm designed for wireless Software Defined Internet of Things by dynamic path relaxation based on real-time heuristic that always gives the best least hop path. This is aimed towards providing an alternative backup network for a smart hospital that functions independently on the failure of the primary hospital network. Given a pair of source and destination nodes, a central controller with a global network view first calculates a minimum hop path greedily by minimizing the remaining distance to destination. Hence, the path so found is relaxed dynamically by substituting certain links with other links keeping the hop count constant if a new path is found with a lesser latency than the previous one. For this, the controller maintains a matrix containing real-time delay between neighboring IoT nodes given the current network state and whose values are periodically refreshed using a heuristic function based on actual delay measurements. This enables the controller to dynamically modify routing paths. Simulation analysis of routeNow shows that it formulates routing paths with significant less time even with large networks and by always maintaining a route with least hops, on average a least latency path is also ensured.

Airports, being high-risk zones, require robust passenger verification systems. However, current cloud-based facial recognition systems often suffer from single-point-of-failure vulnerabilities. To address this, a blockchain-based airport access mechanism is proposed utilizing Ethereum smart contracts. In this system, passengers requesting access have their facial embeddings verified by a distributed set of Ethereum miners. These miners are organized into clusters, with each cluster using a different facial recognition algorithm, thereby enhancing the accuracy of the verification process. Through asymmetric public- key cryptography and secure hashing, miners reach a consensus both within and across clusters on the authenticity of the passenger. Additionally, the system connects to real-time databases to check for any pending warrants or flying restrictions. Access is granted only if all miners successfully verify and authenticate the passenger. By leveraging a reinforced distributed architecture, this solution eliminates single-point-of-failure risks as well being resistant to DDoS attacks, supporting liveness detection and also accounting for ageing. The experimental Proof-of-Concept demonstrated a 5.72 percent increase in detection accuracy with protocol diversification compared to using a single model.

The process of adding new blocks to a blockchain, known as mining, is an energy intensive task. This can be especially challenging over low-power Internet-of-Things (IoT) nodes with limited resources. Any blockchain protocol over IoT must consider the effect of mining on the actual power expenditure and performance of the nodes. In this work, we design a thorough experiment whereby we obtain an empirical understanding of the nature of energy consumption with block mining over IoT. Specifically, considering Proof-of-Work (PoW) based consensus and a set of IoT nodes, we execute the most intensive computation of mining, viz. hashing over those nodes with different block difficulty levels and record the actual energy consumed for each. We plot the data against the difficulty, and for each observation, we obtain a curve of best-fit that captures the generic nature. We infer empirically that the amount of energy consumed for any generic IoT node and for any hash algorithm is parabolic with respect to increasing difficulty. We also find that beyond a certain upper difficulty limit, block mining over IoT nodes becomes infeasible due to indefinite computation time. Our study suggests that using IoT devices for blockchain mining is achievable but requires careful selection and customization of the hashing algorithm and hardware, depending on specific IoT scenarios. Towards efficient blockchain architecture over IoT, our research provides an empirical base that allows the formulation of effective mining strategies.

The future social metaverse traffic demands over 5G and beyond (B5G) networks can be supported by efficiently placing the user plane function (UPF). Third-generation partnership project (3GPP) standard mandates that all application traffic in B5G core network must be routed through a UPF before it can reach its destination. In this regard, we propose an optimal UPF placement algorithm, referred to as Decomposed Minimum Power UPF Placement (DeMPUP), which selects the minimum power consumption path from the next-generation node B (gNB) to the edge server on the overlay backbone network. Coupled with the optimal path selection, the proposed algorithm also identifies the optimal node over which the UPF would be placed. We provide a power consumption model of the core network with a corresponding joint non-linear programming (NLP) minimization problem for UPF placement. Owing to the high complexity of the formulated NLP, for enhanced scalability over large B5G cores, we decompose the problem into two sub-problems. The first sub-problem identifies a set of feasible paths commensurate with UPF power requirement, and the subsequent sub-problem selects the optimum path among the feasible paths that minimizes the power consumption. The selected path also uniquely identifies the node to place the UPF for minimum power consumption. Analysis of DeMPUP shows that it executes in linear time and scales linearly with the number of nodes. Experimental results reveal significant power reduction for different network sizes.

We propose xDIoT, a paradigm for reliable cross-domain communication across separated IoT network domains over some public network such as the Internet. Depending on use-case scenarios, several individual IoT domains, each consisting of heterogeneous end-devices (sensors and actuators) are deployed across geographical regions. When these domains need to communicate with one another they can use an intermediate public network like the Internet. To this end, a standard paradigm is required for such inter-domain communication over public networks to reduce latencies and prevent inconsistencies, which is absent in current deployments. With xDIoT we address this issue to provide a uniform communication paradigm. In xDIoT, each individual domain has an associated gateway access point (AP) acting as the bridge between the intra-domain IoT network and the external public network. These APs perform Domain Information Exchange through JSON format to gain knowledge about each other and use a generalized packet header structure to encapsulate all data flowing between these APs. Through the use of JSON data exchange and the proposed packet header, the APs can perform seamless inter-domain communication. Implementation and analysis show that xDIoT achieves about 10% improvement in total communication latency with 80% improvement in packet processing time at individual gateway APs.

In this paper, we propose a Blockchain-based solution for the recovery of an SDN controller back to a previously known state upon sudden failure. A lightweight minimal Blockchain ledger containing metadata details about each controller event is maintained by the switches. The set of all instructions given by the controller to the switches denotes the state of the controller at that instant. Whenever a new event occurs, the meta-information about it gets stored in the Blockchain which is updated in the switches after regular epochs. Upon sudden failure and subsequently coming back online again, the controller downloads all the tables and information from the respective switches. It checks and compares the metadata contained in the Blockchain with those data received from the switches. In addition to the existing security services provided by Blockchain, the proposed scheme can further solve the controller failure problem. The performance of the proposed solution is measured through simulation. The proposed scheme with the metadata-based solution saves about 75% of space and a controller can securely recover with a duration of 50 Sec.

We propose an idea to speed up instruction execution through a probabilistic approach, using the parallelism offered by quantum computers. For this, we divide the instruction set of an arbitrary quantum instruction set architecture (QISA) into separate groups and then bias certain qubits representing the group so that only the instructions within the group have a high probability of getting executed in a quantum processor. Therefore, the result generated will be the superimposition of the qubits as if all the instructions within the group were executed simultaneously. We show that we can achieve a significant design improvement compared to classical computer.