Millimetre-wave (mmWave) communication systems use large antenna arrays and narrow beams to achieve strong signal power. However, this approach requires extensive beam training, which leads to high overhead. Recently proposed vision-aided beam prediction methods show promising results, reducing this overhead. However, these techniques have considerable computational complexity, hindering practical deployment. To address this issue, we propose a Semantic-Aware Quantised Network (SemQNet) framework that leverages image compression and a lightweight computer vision model to extract semantic information used for training a fully connected neural network (FCNN). Additionally, the proposed SemQNet also uses quantisation-aware training (QAT), which enables low-precision arithmetic operation, reducing the model size in the training process. Our tests on the DeepSense 6G dataset show that SemQNet achieves almost the same top-1 accuracy as existing vision-based methods while reducing the model size by 74.21%. This smaller model size reduces the communication overhead, making SemQNet a practical and efficient solution for energy-constrained mmWave communication systems.

Deploying new optimised routing policies on routers in the event of link failure is difficult due to the strong coupling between the data and control planes and the absence of topology information about the network. Because of the distributed architecture of traditional Internet protocol networks, policies and routing rules are spread in a decentralised way, resulting in looping and congestion problems. Software-defined networking (SDN) enables centralised network programmability. As a result, data plane devices just focus on packet forwarding, leaving the control plane's complexities to be managed by the controller. Thus, the controller centrally installs the policies and rules. Considering the controller's knowledge of the global network architecture, central control enhances the flexibility of link failure identification and restoration. Therefore, this paper uses SDN architecture to enhance network resilience against link failures by introducing the Hybrid Intelligent Fast Failure Recovery (HIFFR) framework, which aims to improve the speed and effectiveness of network failure recovery.

Advancements in wireless communication, en-compassing cellular and non-terrestrial networks, are empow-ering Autonomous Vehicles (AVs) to revolutionize transportation. The achievement of real-time data exchange and seamless communication with infrastructure promises a future of safer and more efficient travel. However, the significant challenge of efficiently managing the extensive data generated by AVs persists. This data includes sensor readings, information about the surrounding environment, and potentially user data. Consequently, addressing concerns related to data processing, sharing among various stakeholders, privacy, integrity, and security is of utmost importance. This paper tackles the data sharing challenge by proposing and evaluating a platform built on Hyperledger Fabric, a blockchain technology. This platform aims to facilitate secure and efficient data sharing between all parties involved with AVs. Our initial testing reveals that the number of simulated users (virtual user count) and the amount of data processed (data load) can negatively impact the system's performance. This highlights the need for further optimization to ensure the platform can handle large-scale data sharing effectively.

This work intends to discover diversified association rules efficiently using a cluster computing model. At first, the input data is pre-processed for data transformation. Then, the pre-processed data is given to k-means clustering to cluster the data. Since the initialization of the centroid is the key feature of k-means clustering, it is taken as the major challenge here. The randomly assigned centroid is optimally tuned by the new Fitness based Probability for Cuckoo Search (FP-CS) model. By exploiting adopted FP-CS, the best k-means centroid is determined. Thus, the optimal centroids are further processed for k-means clustering, and the optimal clustered data is attained. The clustered data is then given as input to the apriori algorithm, and rule mining data is attained in a proficient manner. Moreover, the adopted FP-CS model is evaluated with conservative methods, and the relevant outcomes are verified.

Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the time-bounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applications mixed-criticality QoS requirements in multi-communication networks. We (i) present the network resiliency requirements of IoT applications by defining a system model (ii) analyse and evaluate the bandwidth, latency, throughput, maximum packet size of many state-of-the-art LPWAN technologies, such as Sigfox, LoRa, and LTE (CAT-M1/NB-IoT) and Wi-Fi, (iii) implement and evaluate an adaptive system Resilient Edge and Criticality-Aware Best Fit (CABF) resource allocation algorithm to meet the application resiliency requirements using Raspberry Pi 4 and Pycom FiPy development board having five multi-communication networks. We present our findings on how to achieve 100% of the best-effort high criticality level message delivery using multi-communication networks.

As more and more devices are connected to the Internet-of-Things, often made by non-specialist companies or short-lived startups, the likelihood that these devices will be hacked and used for nefarious activity online increases. We seek to support non-expert users in managing the network behaviour of their IoT devices, and assisting them in handling the cases where those devices are hacked. To do so, we wish to enable anomaly detection at the network level, determining when a device starts behaving unusually. This requires capturing data about how devices behave in a diverse range of real deployments, not just lab environments.

Volunteer computing (VC) or distributed computing projects are common in the citizen cyberscience (CCS) community and present extensive opportunities for scientists to make use of computing power donated by volunteers to undertake large-scale scientific computing tasks. VC is generally a noninteractive process for those contributing computing resources to a project, whereas volunteer thinking (VT) or distributed thinking allows volunteers to participate interactively in CCS projects to solve human computation tasks. In this paper, we describe the integration of three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a job distribution middleware, and CitizenGrid, an online platform for hosting and providing computation to CCS projects. This integration demonstrates the combining of VC and VT to help address the scientific and educational goals of games like VAS. This paper introduces the three tools and provides details of the integration process along with further potential usage scenarios for the resulting platform.

In recent years, the networks of low-power devices have gained popularity. Typically, these devices are wireless and interact to form large networks such as the machine to machine networks, Internet of Things, wearable computing, and wireless sensor networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in a partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.

In a federated database system, each independent party exports some of its data for information sharing. The information sharing in such a system is very inflexible, as all peer parties access the same set of data exported by a party, while the party may want to authorize different peer parties to access different portions of its information. We propose a novel query evaluation scheme that supports differentiated access control with decentralized query processing. Anew efficient join method, named split-join, along with other safe join methods is adopted in the query planning algorithm. The generated query execution reduces the communication cost by pushing partial query computation to data sources in a safe way. The proofs of the correctness and safety of the algorithm are presented. The evaluation demonstrates that the scheme significantly saves the communication cost in a variety of circumstances and settings while enforcing autonomous and differentiated information sharing effectively.