Internet of Things (IoT) is commonly utilized in domestic, industrial, and public environments to automate various tasks. Due to this, an enormous amount of data is being generated and transmitted through IoT networks. These data may contain sensitive information depending on the context. Access control is one of the frontline security measures that any information system should adopt. The dynamic nature of the IoT requires access control policies to be able to adapt to their environments. However, it is very challenging for a human administrator to specify access control policies for all scenarios manually because of their dynamic nature. Current literature suggests the need for automating the process of policy generation. Machine Learning and Deep Learning techniques can enable the required automation. We conducted a case study using two baseline Tabular Generative Adversarial Network (GAN) models, namely CTGAN and CopulaGAN, to generate access control policy data. We utilized the CAV policies dataset published by Cunnington et al. We evaluated our results using both quantitative and manual evaluation. Our initial results identified a significant number of policy violations to underlying environmental constraints. We later trained the models by applying constraints. Our final results demonstrate that the models were able to generate policies without violating the specified environmental constraints.

The security and performance of a microservicebased IoT platform rank among the top significant factors impacting the IoT clients within the network. It is also the case that the energy consumption of such a network has become a critical topic and is greatly under scrutiny. Therefore, it is necessary to design the network to protect against cyberattacks and keep energy consumption at the lowest possible level while maintaining higher system performance. A new Holochain architecture for IoT security is proposed in this paper. A Blockchain network memory usage and energy consumption for IoT data transmission are compared side by side with a Holochain-based IoT-microservice platform. The obtained results demonstrate that the proposed Holochain framework, when the network traffic is over 300 calls, achieves over $60 \%$ less energy consumption, and $\mathbf{5 0 \%}$ more performance which proves better scalability than the Blockchain network.

This article presents a novel cloud-based charging management system for electric vehicles (EVs). Two levels of cloud computing, i.e., local and remote clouds, are employed to meet the different latency requirements of the heterogeneous EVs while exploiting the lower-cost computing in remote clouds. Specifically, we consider time-sensitive EVs at highway exit charging stations and EVs with relaxed timing constraints at parking lot charging stations. We propose algorithms for the interplay among EVs, charging stations, system operator, and clouds. Considering the contention-based random access for EVs to a 4G Long-Term Evolution network, and the quality of service metrics (average waiting time and blocking probability), the model is composed of: queuing-based cloud server planning, capacity planning in charging stations, delay analysis, and profit maximization. We propose and analyze aprice-incentive methodthat shifts heavy load from peak to off-peak hours, acapacity expansion methodthat accommodates the peak demand by purchasing additional electricity, and a hybrid method of price incentives and capacity expansion that balances the immediate charging needs of customers with the alleviation of the peak power grid load through price-incentive based demand control. Numerical results demonstrate the effectiveness of the proposed methods and elucidate the tradeoffs between the methods.

A large scale of electric vehicles (EVs) and the operation of smart grid requires the support of a reliable and robust communication infrastructure. Cloud computing has gained popularity in smart grid for reducing computational and communication complexity. Based on cloud computing services, this paper considers the issues of high charging demand in fast charging stations (FCSs) during peak hours and communication among a large-scale of EVs, a network of FCSs, and system operator (SO). More specifically, we propose a novel cloud-based hierarchical charging management model of EVs, whereby two levels of cloud computing infrastructures are considered to meet different latency requirements of customers in highway exits and parking lots. Considering the quality of service (QoS) metrics (average waiting time in the queue, and blocking probability), the model is composed of: server planning in the cloud, capacity planning in FCSs, and profit maximization. Meanwhile, a price incentive mechanism is applied to shift the heavy load from peak hours to off-peak hours. Numerical results demonstrate the effectiveness of the proposed method, which can guarantee QoS and system profit, thereby more customers can satisfy their charging demand.

The accelerated evolution of Internet of Things (IoT) architectures and their incorporation in vehicles, buildings, or cities provide the ideal environment for the development and optimization of smart services. Under this light, positioning services that harvest location fingerprinting based on received signal strength indications (RSSIs) are widely popular due to the massive data generation that IoT settings provide. However, the labor-intensive and repetitive task of the radio map construction through offline RSSI fingerprint collection prevents such services from becoming standard equipment for future smart facilities. In this paper, we present a location-aware infrastructure that combines a broad sensing layer, edge computing, and centralized cloud federation support. Our setting gives rise to a sensing mechanism that enables in-facility crowdsourcing able to aid fingerprinting localization services. To that end, instead of extensive offline measurements, we use the facility occupants to gather unlabeled RSSI samples. To support the localization functionality, we develop a probabilistic cell-based model that is constructed by an unsupervised learning algorithm. Our black-box approach maintains the positioning accuracy regardless of changes in the underlying hardware or indoor environment. To evaluate our approach, we have deployed a multistorey facility testbed and performed an extensive real-subject trial to gather the unlabeled fingerprint dataset. The proposed unsupervised method yields average location classification accuracy of 0.8 that can rise up to 0.9 when a semi-supervised approach is considered. We also provide insights into the performance of the proposed infrastructure regarding mobility tracking, and under varying deployment scenarios.

The advent of Internet of Things and 5G applications renders the need for integration of both centralized cloud computing and emerging mobile-edge computing (MEC) with existing network infrastructures to enhance storage, processing, and caching capabilities in not only centralized but also distributed fashions for supporting both delay-tolerant and mission-critical applications. This paper investigates performance gains of centralized cloud and MEC enabled integrated fiber-wireless (FiWi) access networks. A novel unified resource management scheme incorporating both centralized cloud and MEC computation offloading activities into the underlying FiWi dynamic bandwidth allocation process is proposed. Both MEC and cloud traffic are scheduled outside the transmission slot of FiWi traffic by leveraging time division multiple access. An analytical framework is developed to model packet delay, response time efficiency, gain-offload overhead ratio, and communication-to-computation ratio for both cloud and broadband access traffic. In addition, given the importance of reliability in optical backhaul and MEC, this paper develops a probabilistic survivability analysis model to assess the impact of both fiber cuts and MEC server failures. The obtained results demonstrate the feasibility of implementing conventional cloud and MEC in FiWi access networks, without affecting network performance of broadband access traffic.

Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource management in the context of multi-tenant cloud computing is becoming one of the most complex task due to the inherent heterogeneity and resource isolation. This paper proposes a novel cloud-based workflow scheduling (CWSA) policy for compute-intensive workflow applications in multi-tenant cloud computing environments, which helps minimize the overall workflow completion time, tardiness, cost of execution of the workflows, and utilize idle resources of cloud effectively. The proposed algorithm is compared with the state-of-the-art algorithms, i.e., First Come First Served (FCFS), EASY Backfilling, and Minimum Completion Time (MCT) scheduling policies to evaluate the performance. Further, a proof-of-concept experiment of real-world scientific workflow applications is performed to demonstrate the scalability of the CWSA, which verifies the effectiveness of the proposed solution. The simulation results show that the proposed scheduling policy improves the workflow performance and outperforms the aforementioned alternative scheduling policies under typical deployment scenarios.

This paper proposes to enhance capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with computation- and storage-centric cloudlets to provide reliable cloud services at the edge of FiWi networks and thereby realize the vision of mobile-edge computing (MEC). To reduce offload delay and prolong battery life of edge devices, a novel cloudlet-aware resource management scheme is proposed that incorporates offloading activities into the underlying FiWi dynamic bandwidth allocation process. The whole system is designed in two time division multiple access layers to enhance the network performance. To allow for the efficient coexistence of FiWi and MEC traffic, the offloaded traffic is scheduled outside the FiWi transmission slots. To thoroughly study the scheme's performance, a comprehensive analytical framework is developed that covers a rich set of performance metrics, including packet delay of both FiWi and MEC traffic, response time efficiency, offload gain-overhead ratio, energy efficiency, and battery life. Analytical results demonstrate the feasibility and effectiveness of the cloudlet-enhanced FiWi networks for MEC by employing the proposed solution. Further, we develop an experimental testbed to validate the accuracy of our analytical model via real-world measurements.

This paper aims at designing an ecoconscious future-proof sensor enhanced fiber-wireless (SFiWi) network based on EPON, WLAN, wireless sensor (WS), and fiber optic sensor (FOS) technologies as a shared communications infrastructure for broadband access and smart grids. A total cost of ownership (TCO) model is developed to help utilities decide whether to deploy WSs or FOSs in different scenarios and estimate sensor-related costs. To prolong battery life of wireless devices and maximize the overall energy efficiency, a novel energy conservation scheme for SFiWi networks (ECO-SFiWi) is proposed. ECO-SFiWi designs the whole network in three TDMA layers to enhance network performance, while scheduling network components to sleep outside their transmission slots. A comprehensive energy saving model accounting for both optical backhaul and wireless front-end components and a delay analysis based on M/G/1 queuing are presented. Results reveal that with their extremely long lifetime and ability to sustain in harsh environments, FOSs are superior to WSs when advanced interrogation techniques are deployed to reduce their total cost. ECO-SFiWi achieves more than 89% of energy savings, while maintaining low delay for both broadband and smart grid traffic in typical scenarios. FPGA hardware emulation and analytical results match well verifying the effectiveness of ECO-SFiWi.

Fiber-wireless sensor networks (Fi-WSNs) composed of a hybrid fiber-wireless (FiWi) network enhanced with sensors will play a key role in supporting machine-to-machine (M2M) communications to enable a wide range of Internet of Things (IoT) applications, of which smart grids represent an important real-world example. This paper explores opportunities of designing an energy-efficient Fi-WSN based on EPON/10G-EPON, WLAN, wireless sensors, and passive fiber optic sensors as a shared communications infrastructure for broadband services and smart grids. A novel energy conservation scheme for sensor enhanced FiWi networks (ECO-SFiWi) is proposed to reduce the overall energy consumption. ECO-SFiWi maximizes energy efficiency by leveraging TDMA to schedule power-saving modes of EPON's optical network units, wireless stations, and wireless sensors and incorporate them into EPON's bandwidth allocation algorithm. To study the performance, a comprehensive energy saving model and a delay analysis of both FiWi traffic and sensor data based on M/G/1 queue modeling are presented. FPGA-based hardware emulation and demonstration are performed to verify the effectiveness of the proposed solution. Results provide deep insights into the tradeoff between energy savings and frame delays. Noticeably, ECO-SFiWi achieves significant amounts of energy saving, while maintaining low delay for FiWi traffic and sensor data under typical deployment scenarios.

Integrated fiber-wireless (FiWi) access networks aim at taking full advantage of the reliability and high capacity of the optical backhaul along with the flexibility, ubiquity, and cost savings of the wireless/cellular front-end to provide broadband services for both mobile and fixed users. In FiWi access networks, energy efficiency issues must be addressed in a comprehensive fashion that takes into account not only wireless front-end but also optical backhaul segments to extend the battery life of wireless devices and allow operators to reduce their OPEX, while not compromising quality of service (QoS). This paper proposes an energy conservation scheme for FiWi networks (ECO-FiWi) that jointly schedules power-saving modes of wireless stations and access points and optical network units to reduce their energy consumption. ECO-FiWi maximizes the overall network performance by leveraging TDMA to synchronize the power-saving modes and incorporate them into the dynamic bandwidth allocation (DBA) process. A comprehensive energy saving model and an M/G/1 queuing-based analysis of downstream and upstream end-to-end frame delays are presented accounting for both backhaul and front-end network segments. Analytical results show that ECO-FiWi achieves significant amounts of energy saving, while preserving upstream delay and incurring a low delay for downstream traffic.