This study presents a simulated transceiver with a microstrip patch antenna (MPA) designed to resonate at 150 GHz and embedded in paint. The in-paint MPA (IP-MPA) is designed for the Internet of Paint (IoP) paradigm, which envisions seamless device communication through a paint layer on walls. This study introduces a comprehensive channel model for transceivers in paint at arbitrary depths and IP-MPA orientations. The best antenna orientations are analyzed for IoP channel performance. Extensive simulations indicate that the lateral waves, which propagate along the air-paint interface, exhibit the lowest loss, making this path the most reliable for communication between transceivers in paint. Furthermore, the maximum received power for each propagation path, except for the direct path, depends on depth. The findings suggest that the proposed network of IP-MPA-enabled transceivers for IoP has the potential to transform conventional walls into an integrated high-speed wireless communication and sensing infrastructure.

This work opens a new chapter in the 100, 000 year-old concept of paint, by leveraging innovations in nano-technology in the sub-THz frequency range. More specifically, the groundbreaking concept of Internet of Paint (IoP) is introduced along with a comprehensive channel model and a capacity analysis for nano-scale radios embedded in paint and communicating through paint. Nano-network devices, integrated within a paint medium, communicate via a multipath strategy, encompassing direct waves, reflections from interfaces, and lateral wave propagation. The evaluation incorporates three distinct paint types to assess path losses, received powers, and channel capacity. Analysis of path loss indicates a slight non-linear increase with both frequency and Line of Sight (LoS) distance between transceivers. Notably, paints with high refractive indexes result in the highest path loss. Moreover, burying transceivers at similar depths near the Air-Paint interface showcases promising performance of lateral waves with increasing LoS distance. Increasing paint layer depth leads to amplified attenuation, while total received power exhibits promising results when in close proximity to the Air-Paint interface but steeply declines with burial depth. Additionally, a substantial reduction in channel capacity is observed with LoS distance and burial depth, so transceivers need to be close together and in proximity of the A-P interface to communicate effectively. Comparing paint and air mediums, IoP demonstrates approximately two orders of magnitude reduction in channel capacity compared to air-based communication channels. This paper provides valuable insights into the potential of IoP communication within paint mediums and offers a foundation for further advancements in this emerging field.

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computing-based service placement scheme.

As more and more services are deployed on devices near the network edge, security operations (such as authentication and authorization) need to move with them. Typically, edge devices have fewer resources than data center servers and so the security operations need to make more efficient use of what is available while offering adequate performance. Authorization adds latency and requires system resources, but the need for security management with strong authorization at the network edge is growing. We have released the first open source, high-performance, resource-efficient, XACML3 standard-compatible Policy Decision Point (PDP) called Luas (means “speed'' in the Irish language) based on an event-driven architecture and a non-blocking computational model, using a Bloom Filter for better performance. We compared its performance, resource usage and reliability against existing open source PDPs. Like those we tested, it provides accurate decisions, but Luas offers much faster security policy evaluation while using fewer system resources, and provides responses in a reasonable timeframe even when resources are scarce.

Managing network resources in highly dynamic vehicular environments is difficult. It is made more challenging when vehicular safety applications demand high bandwidth, reliability and extremely low latency. Standalone wireless access technologies cannot meet these requirements simultaneously and so heterogeneous wireless networking has been proposed as a solution. However heterogeneity introduces other problems, particularly network fragmentation and inefficient use of network resources. Software-defined Networking (SDN) offers logically centralized control and re-programmability of the control plane, with the potential to address these problems of heterogeneity. However, SDN has difficulty dealing with the high levels of mobility associated with highly dynamic vehicular networks. This paper presents ongoing work towards an SDN-based heterogeneous vehicular networking, employing edge-based caching to ensure extremely reliable and low-latency communication for safety-critical cooperative vehicular services.

Vehicles are an important source of data, with embedded sensors including built-in cameras. Their data can be processed close to where it was generated, using computation and network resources on collaborating vehicles. Processing at the network edge helps to reduce end-to-end service latencies. However, such mobile virtual resource pools require effective management, especially if those resources are to be leased to third-party service providers. We address the problem of service placement on moving, connected, vehicle nodes (V2V) supported by roadside infrastructure (V2I). We formulate a distributed service model and optimize the placement of services, subject to constraints related to node resource capacity, link capacity, distributed application deployment (full deployment, anti-collocation and adjacency constraint) and vehicle mobility.

As the need for more pervasive and more complex access controls grows, the challenge of ensuring the performance of access control systems is becoming apparent. Researchers have proposed several solutions to mitigate performance problems, including: adjusting the policy set; re-engineering the policy decision point (PDP); and decomposing the policies and distributing their evaluation. However, since the benefits and tradeoffs depend heavily upon the actual scenario, security administrators typically do not have objective justification for adopting particular mitigation actions. In response, we present the ATLAS framework, comprising: 1) DomainManager, which facilitates modelling the domain as closely as possible and automatically generates large numbers of representative policies and associated requests; 2) STACS, which enables controlled experiments to be performed using the generated policies/requests, to collect comprehensive measurements of PDP performance; and 3) PARPACS, which aids the understanding and worth of the measurement data and, by using rigorous validation techniques, reduces the risk of spurious insights or incorrect recommendations. We present a discussion of ATLAS as applied to an enterprise communication scenario, where access control is realised via XACML PDPs. Notable insights include that the SunXacml 2.0 PDP performs relatively poorly in terms of policy evaluation performance and that adding additional memory and/or processor cores to a XACML PDP server is not guaranteed to improve performance significantly.

The performance and scalability of access control systems is a growing concern as organisations deploy ever more complex communications and content management systems. This paper describes how an (offline) experimental testbed may be used to address performance concerns. To begin, timing measurements are collected from a server component incorporating the Policy Decision Point (PDP) under test, using representative policies and corresponding requests. Our experiments with two XACML PDP implementations show that measured request service times are typically clustered by request type; thus an algorithm for request cluster identification is presented. Cluster characterisations are used as inputs to a PDP performance model for a given policy/request mix and an analytic (queueing) model is used to estimate the equilibrium server load for different mixes of request clusters. The analytic performance prediction model is validated and extended by discrete event simulation of a PDP subject to additional load. These predictive models enable network administrators to explore the capacity of the PDP for different overall loadings (requests per unit time) and profiles (relative frequencies) of requests.

The performance and scalability of access control systems is growing more important as organisations deploy ever more complex communications and content management systems. Fine-grained access control is becoming more pervasive, so decisions are more frequent and policy sets are larger. We outline a flexible performance testing framework that accepts XACML PDP implementations (in the server component) and submits representative access control requests (from the client component) in a representative temporal ordering. The framework includes instrumentation and analysis modules to support performance experiments. We describe an initial realization of the framework and report on initial experiments comparing the performance of the SunXACML and Enterprise XACML PDPs.