Accurate prediction of cellular link performance represents a corner stone for many adaptive applications, such as video streaming. State-of-the-art solutions focus on distributed device-based methods relying on historic throughput and PHY metrics obtained through device APIs. In this paper, we study the impact of centralised solutions that integrate information collected from other network nodes. Specifically, we develop and compare machine learning inference engines for both distributed and centralised approaches to predict the LTE physical resource blocks using ns3-simulation. Our results illustrate that network load represents the most important feature in the centralised approaches resulting in halving the RB prediction error to 14% in comparison to 28% for the distributed case.

In the coming 6G communications, the internet of things (IoT) is the core foundation technology for various key areas. According to related studies, the number of IoT sensors deployed in 6G will be approximately 10 times larger than that in 5G. Therefore, IoT sensors in 6G are required to have extremely low cost, low power consumption, and high robustness so that they can effectively sustain massive deployment. However, traditional sensors containing costly and vulnerable fine structures and requiring external power sources can hardly meet the above requirements. Fortunately, meta-material IoT (meta-IoT) sensors are simple in structure, low in cost, and can obtain power supply via wireless signals, showing great potential for application.

However, existing meta-IoT sensing systems are limited to normal or specular reflection, while in practical sensing scenarios such as smart home, intelligent industry, transportation, and agriculture, the receivers are often deployed on movable objects such as mobile phones, intelligent robots, and vehicles. Thus, the position of the receivers relative to the meta-IoT sensor array is generally dynamic within an angular range rather than at a particular angle, which is challenging as all units in existing meta-IoT sensors are assumed to be the same, resulting in an uncontrollable reflection direction. To address this challenge, we propose a design of a meta-IoT sensing system comprising meta-IoT sensors that can support transmitter deployment at any given angle and receiver deployment in an extended angular range.

This work presents MatGAN, a system that uses millimeter-wave (mmWave) signals to capture high-quality images of a person's body while they sleep, even if they are covered under a blanket. Unlike existing sleep monitoring systems, MatGAN enables fine-grained monitoring and is privacy non-invasive, and can work under obstruction and low-light conditions, critical for sleep monitoring. MatGAN utilizes generative models to generate high-quality images from mmWave reflected signals that accurately represent sleep postures under a blanket. Early results indicate that MatGAN can effectively generate sleep posture images with a median IoU of 0.64.

We study the performance of scalable cell-free massive MIMO (CF-mMIMO) in practical urban network topologies in the cities of Frankfurt and Seoul. Our results show that the gains of CF-mMIMO - in terms of the high and uniform network throughput - are limited in practical urban topologies, compared with what is expected from theory for randomly uniformly distributed networks. We show that this is due to the locally non-uniform spatial distributions of access points - characteristic of realistic network topologies, resulting in inferior throughput, especially for the worst-served users in the network.

Synchronizing the system time between devices by exchanging timestamped messages over the network is a popular method to achieve time-consistency in distributed applications. Accurate time synchronization is essential in applications such as cellular communication, industrial control, and transactional databases. These applications consider the maximum possible time offset or the Time Uncertainty Bound(TUB) in the network while configuring their guard bands and waiting times. Choosing the right value for the TUB poses a fundamental challenge to the system designer - a conservatively high value of the TUB decreases the chances of time-based byzantine faults but increases latency due to larger guard bands and waiting times. The TUB is affected by packet delay variation(PDV) of the time synchronization messages due to congestion from background network traffic. In this work, we use Network Calculus (NC) to derive the relation between network traffic and the TUB for a network built with commercial off-the-shelf (COTS)hardware. For centrally deployed and monitored local area networks (LAN)s such as in cellular networks and datacenters, this relation could be useful for system designers to plug a better-informed value of the TUB.

Layer-7 load balancing is an essential pillar in modern enterprise infrastructure. It is inefficient to scale software layer-7 load balancing which requires hundreds of servers to meet the large scale service requirements of 1Tbps throughput and 1M concurrent requests. In this paper, we present L7LB with a novel fast path and slow path co-design architecture running on the heterogeneous programmable server-switch. L7LB is scalable by forwarding most data packets on the Tbps bandwidth switch chip and using CPU to process application connections and efficient by replacing hundreds of servers with one server-switch. The preliminary prototype demonstrates the layer-7 load balancing functionality and shows that L7LB can meet the large scale service requirements.

The COVID-19 pandemic have significantly impacted people by driving people working from home using communication tools such as Zoom, Teams, Slack, \textit{etc}. The users of these communication services have exponentially increased in the past two years, e.g., Teams annual users reached 270 million in 2022 and Zoom averaged 300 million daily active users in videoconferencing platforms. However, using edging artificial intelligence techniques, new cyber attacking tools expose these services to eavesdropping or disruption. This work investigates keystroke eavesdropping attacks on physical keyboards using deep learning techniques to analysis the acoustic emanation of keystroke audios to identify victims' keystrokes. An accurate context-free inferring algorithm was developed that can automatically localise keystrokes during inputs. The experimental results demonstrated that the accuracy of keystroke inference approaches around 90\% over normal laptop keyboards.

Artificial intelligence (AI) methods have been widely applied for accurate network intrusion detection (NID). However, the developers and users of the NID systems could not understand the systems' correct or incorrect decisions due to the complexity and black-box nature of the AI methods. This is a two-page poster paper that presents a new demo system that offers a number of counterfactual explanations visually for any data example. The visualization results were automatically generated: users just need to provide the index of a data example and do not edit anything on the graph. In the future, we will extend the detection task from binary classification to multi-class classification.

This paper studies a self-sustainable reconfigurable intelligent surface (SRIS)-assisted mobile edge computing (MEC) network, where a SRIS first harvests energy from a hybrid access point (HAP) and then enhances the users' offloading performance with the harvested energy. To improve computing efficiency, a sum computation rate maximization problem is formulated. Based on the alternating optimization (AO) method, an efficient algorithm is proposed to solve the formulated non-convex problem. Simulation results show that when the SRIS is deployed closer to the HAP, a higher performance gain can be achieved.

Auto-scaling is a well-known mechanism for adapting systems to dynamic loads of traffic by increasing (scale-up) and decreasing (scale-down) the number of handling resources automatically. As software development shifted to micro-services architecture, large software systems are nowadays composed of many independent micro-services, each responsible for specific tasks. The breakdown to fragmented applications influenced also on the infrastructure side where different services of the same application are given different hardware configurations and scaling properties. Even though created to accelerate software development the micro-services approach also presents a new challenge - as systems grow larger, incoming traffic triggers multiple calls between micro-services to handle each request.