Majid Iqbal Khan

Future quantum networks will have to support different applications on shared infrastructure, like fidelity-critical quantum key distribution (QKD) and latency-sensitive distributed quantum computing (DQC). The existing approaches for entanglement routing either optimise for a single application, or rely on sequential resource allocation, which results in inefficient resource utilisation, unfairness and degraded performance when multiple applications compete for shared network resources. In this paper, we propose AMARQ, a multi-objective deep Q-network based routing framework to jointly serve heterogeneous QKD and DQC traffic on shared quantum infrastructure. The agent makes adaptive and application-aware routing decisions to dynamically balance the rigid fidelity and latency requirements of QKD and the high-throughput and elastic rate requirements of DQC. The reward function combines a successful-routing throughput term with a per-edge fidelity penalty and an end-of-episode failure penalty; per-flow latency is enforced as a QoS constraint, and inter-application fairness is enforced by a dedicated admission-control loop that reserves a dynamic QKD qubit pool. We assess the performance of AMARQ on grid topologies with 25 to 625 nodes. AMARQ attains a request success ratio above 85%, which is up to 32 percentage points higher than ESDI and 39 percentage points higher than Cicconetti, while keeping the end-to-end fidelity above 0.80 and the inference latency below 90 ms for all configurations.

Advancements in sensor-enabled devices led to the emergence of resource trading models for smart communities, such as the smart marketplace (SMP). Most of the proposed SMP architectures are based on blockchain technology, which has a public ledger to achieve transparency. Consequently, safeguarding the participant’s anonymity, untraceability, and transactional data privacy during trading becomes a challenging task. Most of the existing solutions to achieve anonymity are based on multiple account mapping, which is prone to identity-based attacks, and cryptographic techniques are used to achieve transactional data privacy, which often has a high computational overhead. In this work, we propose a lightweight privacy-enabled message exchange mechanism to accomplish our privacy goals in a blockchain-based SMP. Evaluation of the scheme was conducted to measure its resilience toward safeguarding participants’ anonymity, untraceability, and transactional data privacy during trading cycles. Statistical game theory-based security analysis and simulation based performance analysis of the proposed scheme showed that it achieved the desired privacy goals with a low computational overhead compared with existing state-of-the-art schemes.

The dynamic host configuration protocol (DHCP) servers are forms of an Internet of Things (IoT) address management server (IoTAMS) that gives network configuration settings to newly connected hosts. Administrators of a network may save time by setting DHCP servers instead of every network node. However, the absence of a more robust authentication method for DHCP servers makes hosts susceptible to attacks since neither the server nor the users are able to check the other’s authenticity during DHCP connections. These concerns result in both internal and external threats to the system that have the potential to impair network services. Among these threats are malicious DHCP servers and DHCP starvation. This paper aims to provide a novel approach for tackling these issues and protect the DHCP protocol. The proposed model uses the Diffie–Hellman key exchange mechanism, the elliptic curve discrete logarithm problem (ECDLP), a one-way hash function, blockchain technology, and a smart contract. In addition, registration and validation processes provide support for the proposed model in combating DHCP risks for both internal and external system threats. Results from this study show that the proposed model has an average of 21.1% more resistance to a growing number of adversaries than the benchmark models, thus revealing that the model is better suited for the security of IoT address management servers in smart homes, thereby enhancing resilience against related threats and the success of IP address management.

With the innovation of embedded devices, the concept of smart marketplace came into existence. A smart marketplace is a platform on which participants can trade multiple resources, such as water, energy, bandwidth. Trust is an important factor in the trading platform, as the participants would prefer to trade with those peers who have a high trust rating. Most of the existing trust management models for smart marketplace only provide a single aggregated trust score for a participant. However, they lack the mechanism to gauge the level of commitment shown by a participant while trading a particular resource. This work aims to provide a fine-grained trust score for a participant with respect to each resource that it trades. Several parameters, such as resource availability, success rate, and turnaround time are used to gauge the participant’s level of commitment, specific to the resource being traded. Moreover, the effectiveness of the proposed model is validated through security analysis against ballot-stuffing and bad-mouthing attacks, along with simulationbased analysis and a comparison in terms of accuracy, false positive, false negative, computational cost and latency. The results indicate that the proposed trust model has 7% better accuracy, 30.13% lower computational cost and 31.74% less latency compared to the existing benchmark model.

The use of real-time systems is growing at an increasing rate. This raises the power efficiency as the main challenge for system designers. Power asymmetric multicore processors provide a power-efficient platform for building complex real-time systems. The utilization of this efficient platform can be further enhanced by adopting proficient scheduling policies. Unfortunately, the research on real-time scheduling of power asymmetric multicore processors is in its infancy. In this research, we have addressed this problem and added new results. We have proposed a dynamic-priority semi-partitioned algorithm named: Earliest-Deadline First with C=D Task Splitting (EDFwC=D-TS) for scheduling real-time applications on power asymmetric multicore processors. EDFwC=D-TS outclasses its counterparts in terms of system utilization. The simulation results show that EDFwC=D-TS schedules up to 67% more tasks with heavy workloads. Furthermore, it improves the processor utilization up to 11% and on average uses 14% less cores to schedule the given workload.