Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers’ goal of low deployment costs.

Spatial and temporal correlation among the generated traffic in wireless sensor networks (WSNs) can be exploited in reducing the energy consumption of continuous sensor data collection. Dual Prediction (DP) and Data Compression (DC) schemes rely on the spatio-temporal correlation to reduce the number of transmissions across WSNs, which leads to conserving energy and bandwidth. In this paper, we present both schemes in a two-tier data reduction framework. The DP scheme is used to reduce transmissions between cluster nodes and cluster heads, while the DC scheme is used to reduce traffic between cluster heads and sink nodes. For both schemes, various algorithms will be studied and compared in terms of accuracy, delay, and transmission reduction percentage. For the DP scheme, Neural Networks (NNs) and Long Short-Term Memory networks (LSTMs) are proposed to perform predictions. The training phase of the NNs and LSTMs is done online which is necessary in the DP scheme. The performance will be compared to popular Least-Mean-Square (LMS) approaches. Regarding the DC scheme, Principal Component Analysis (PCA), Non- Negative Matrix Factorization (NMF), Truncated-Singular Value Decomposition (T-SVD), and Discrete Wavelet Transform (DWT) will be discussed and compared. This work focuses on comparative analysis of various data reduction algorithms alongside the proposed ones. Finally, design challenges and open research areas for having more transmission reductions will be presented.

Public safety networks (PSNs) are very crucial for Public Protection and Disaster Relief (PPDR). Land Mobile Radio (LMR) technologies have been used extensively in the deployment of PSNs so far. LMR networks support sophisticated voice applications that can, to some extent, deal with the mission-critical nature of PPDR services. However, LMR networks lack technological advancements to support broadband applications. Due to this limitation, the attention is drawn to the Long-Term Evolution (LTE) technology for Public Safety (PS) deployment as it has the potential to support various narrowband and broadband applications and services. LTE-based PSNs should have strict requirements in terms of scalability, robustness, and resilience. In this survey, we will highlight the history of PSNs, including LMR and LTE-based PSNs, discuss the requirements that have to be inherited in PSNs, and examine the spectrum allocated for PS use. Moreover, we will study the architecture of LTE-based PSNs and provide deployment and migration solutions. Furthermore, voice delivery over LTE and LTE standardized solutions tailored to support PS services are discussed. Finally, rapid emergency deployment, spectrum management, priority management, and radio resource management schemes in LTE-based PSNs are discussed as well. At the end of the survey, we present PSNs simulation environment using Network Simulator (NS-3) and provide the results of multiple disaster scenarios.

The wide adoption of large-scale Internet of Things (IoT) systems has led to an unprecedented increase in backhaul (BH) traffic congestion, making it critical to optimize traffic management at the network edge. In IoT systems, the BH network is supported by various backhauling technologies that have different characteristics. Also, the characteristics of the BH links can be sometimes time varying and have an unknown state, due to external factors such as having the resources shared with other systems. It is the responsibility of the edge devices to be able to forward IoT traffic through the unknown-state BH network by selecting the suitable BH link for each collected data flow. To the best of our knowledge, this type of BH selection problem is not addressed in the literature. Therefore, there is a crucial need to develop intelligent approaches enabling edge devices to learn how to deal with unknown-state (partially observable) components of the BH network, which is the primary goal of this article. We propose an edge-based BH selection technique for improving traffic delivery by exploiting multiobjective feedback on delivery performance. The proposed approach relies on the advantage-actor–critic deep reinforcement learning (DRL) methods. Moreover, to improve the DRL training performance in large-scale deployments of distributed IoT systems, federated learning (FL) is applied to enable multiple edge devices to collaborate in training a shared BH selection policy. The proposed federated DRL (F-DRL) approach is able to solve the BH selection problem as verified and demonstrated through extensive simulations.

This article summarizes the European Union (EU) activities in the ACTS program satellite component of the UMTS. The projects INSURED, NEWTEST, SECOMS, SINUS, and TOMAS are introduced, and their R&D work on the services implemented in the different test platforms, the network integration scenario into the UMTS network, the studies performed for the air interface, the mobile terminals implemented, and the demonstrated applications are summarized. Furthermore, a new approach using neural networks for adaptive channel equalization and the concept for an EHF/Ka-band system are introduced.

Internet of Things (IoT) systems are driven by the massive data generation at the sensing layer. Most of data management protocols in the sensing layer target optimizing different Quality-of-Service (QoS) metrics, such as data rate, packet loss, and delay, while taking the limitations of wireless sensing networks (WSNs) into consideration. However, it is also critical to consider improving the quality of collected data, especially when the WSNs are congested by delay-sensitive data packets, leading to high packet loss. While Value-of-Information (VoI), which represents data freshness and time relevance, is commonly used to define data value, it does not provide a notion of data recoverability and tolerance-to-loss. In this article, the main contribution is to develop data management schemes to cope with inevitable data loss by identifying the data portion with a higher value to the overlaying applications. We also develop a novel metric that is referred to as information content (IC), quantifying the amount of information in data. The IC is defined such that data holding information of low-probable events have higher IC than data holding information of high-probable events. In this context, the VoI and IC are exploited in developing information-oriented traffic forwarding and reduction schemes to ensure that all dropped packets are more accurately recoverable through a traffic recovery scheme, and therefore the running applications are not disrupted. Through extensive Monte-Carlo simulations, we show that the proposed information-oriented data management improves the performance in terms of data congestion, lifetime, packet loss, delay, and data recovery accuracy.