5G networks promise to transform our technology experience by delivering ultra-high speeds and low latency, enabling applications like Augmented Reality (AR) and Connected Autonomous Vehicles (CAVs). However, 5G’s higher frequencies reduce its range and lead to performance inconsistencies, especially for users on the move. Moreover, the energy consumption of 5G is significantly higher than its predecessor, 4G, raising sustainability concerns. In this paper, we explore a solution that combines the strengths of Integrated Sensing and Communication (ISAC) with the advanced analytics capabilities of the Network Data Analytics Function (NWDAF) in 5G networks. We leverage two new functions, Sensing Service Function (SSF) and Energy Efficiency Control Function (EECF), designed to work together to make smarter, more energy-efficient network decisions. By optimizing base station downlink transmit power, our approach not only reduces energy consumption but also carefully balances the trade-offs between latency and energy efficiency. Our findings suggest a promising path toward a greener and more reliable future for 5G and beyond networks.

In the realm of modern wireless networks, the integration of wireless sensing and communication systems is pivotal, especially in the context of the forthcoming 6G Internet of Things (IoT) paradigm. The popularity of integrated sensing and communication (ISAC) stems from its potential to amplify the utilization of existing network infrastructures. This study introduces an innovative fusion of joint communication and sensing (JCAS), a framework of ISAC, combined with end-to-end (E2E) network slicing (NS) techniques. The aim is to meet user Quality-of-Service (QoS) expectations within the ambit of 6G IoT applications. The prime objective is to optimize resource allocation for communication and sensing services within a bespoke network slice tailored for 6G IoT scenarios. This is achieved by minimizing E2E system latency, essential for real-time decision making in 6G IoT environments. The optimization challenge is tackled by using deep reinforcement learning (DRL) in the form of a deep$Q$network (DQN) algorithm, which is adept at addressing nonlinear integer programming (NLIP) issues intrinsic to 6G IoT settings. Comprehensive simulations validate the approach, demonstrating its effectiveness in the context of 6G IoT networks. The amalgamation of ISAC with E2E NS emerges as a potent strategy for furnishing enhanced services customized for 6G IoT applications, successfully fulfilling consumers’ QoS requisites. This integrated approach holds substantial promise as a robust solution for addressing the exacting demands of forthcoming wireless networks, particularly those underpinned by the strides in 6G IoT technologies.

An end-to-end (E2E) network slicing (NS) scheme for heterogeneous network (HetNet) is proposed in which the number of slices and instances of various network functions (NFs) are optimized contingent on the number of users (UEs) and their quality of service (QoS) requirements. The objective of the scheme is to empower future generation networks by considering control signaling in the control plane as well as the UE traffic in the user plane of the core network (CN). We formulate a joinT UE assOciation, wiReless bandwidth allocation, sliCe formation, slice assignment, virtual network function (VNF) placement, computing resource allocation, link assignment, and link bandwidtH allocation (TORCH) problem to minimize the E2E task completion time of all UEs while considering both control signaling and UEs’ traffic. Since TORCH is a mixed-integer nonlinear problem, to tackle the problem, we decompose it into two sub-problems: the link assignment problem and the UE Association, resource allocation, Slice formation, Slice AssIgnment, and VNF pLacement (ASSAIL) problem. The ASSAIL problem comprises both the core network (CN) and radio access network (RAN), and they do not compete for resources, so we decompose it into two sub-problems: the RAN problem and the CN problem. We use Dijkstra’s algorithm and a deep Q-learning network (DQN) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the TORCH problem.

Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g., cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Cognitive radio (CR) can be leveraged to mitigate the spectrum scarcity problem of Internet of Things (IoT) applications while wireless energy harvesting (WEH) can help reduce recharging/replacing batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an access point. More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency of the network. In particular, we formulate a mixed integer nonlinear programming problem that maximizes EE while taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints, and interference constraints. We further prove that the proposed optimization framework is an NP-hard problem. Thus, we propose a low complexity heuristic algorithm, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.

The concept of fog computing is centered around providing computation resources at the edge of the network, thereby reducing the latency and improving the quality of service. However, it is still desirable to investigate how and where at the edge of the network the computation capacity should be provisioned. To this end, we propose a hierarchical capacity provisioning scheme. In particular, we consider a two-tier network architecture consisting of shallow and deep cloudlets and explore the benefits of hierarchical capacity provisioning based on queuing analysis. Moreover, we explore two different network scenarios in which the network delay between the two tiers is negligible and the case that the deep cloudlet is located somewhere deeper in the network and thus the delay is significant. More importantly, we model the first network delay scenario with bufferless shallow cloudlets and the second scenario with finite-size buffer shallow cloudlets, and formulate an optimization problem for each model. We also use stochastic ordering to solve the optimization problem formulated for the first model and an upper bound-based technique is proposed for the second model. The performance of the proposed scheme is evaluated via simulations in which we show the accuracy of the proposed upper bound technique and the queue length estimation approach for both randomly generated input and real trace data.

With the fast development of Internet of Things (IoT), the fifth generation (5G) wireless networks need to provide massive connectivity of IoT devices and meet the demand for low latency. To satisfy these requirements, nonorthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. In parallel with the development of NOMA techniques, mobile edge computing (MEC) is becoming one of the key emerging technologies to reduce the latency and improve the quality of service (QoS) for 5G networks. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing aware NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users' uplink energy consumption. To this end, we formulate an NOMA-based optimization framework which minimizes the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit powers. In particular, similar to frequency resource blocks (RBs), we divide the computing capacity available at the cloudlet to computing RBs. Accordingly, we explore the joint allocation of the frequency and computing RBs to the users that are assigned to different order indices within the NOMA clusters. We also design an efficient heuristic algorithm for user clustering and RBs allocation, and formulate a convex optimization problem for the power control to be solved independently per NOMA cluster. The performance of the proposed NOMA scheme is evaluated via simulations.

The multitiered concept of Internet of Things (IoT) devices, cloudlets, and clouds is facilitating a user-centric IoT. However, in such three tier network, it is still desirable to investigate efficient strategies to offer the computing, storage, and communications resources to the users. To this end, this paper proposes a new hierarchical model by introducing the concept of field, shallow, and deep cloudlets where the cloudlet tier itself is designed in three hierarchical levels based on the principle of LTE-advanced backhaul network. Accordingly, we explore a two time scale approach in which the computing resources are offered in an auction-based profit maximization manner and then the communications resources are allocated to satisfy the users' quality of service.

Edge detection is one of the most important concepts used in processing of remote sensing images. The aim of edge detection is to mark the points of an image at which the rate of brightness changes sharply. Sharp changes in image features often represent important events and changes in environmental properties. In other words, edges can be defined as the boundary between two regions separated by two relatively distinct grey level properties. Most classic mathematical methods for edge detection are based on deriving original image pixels such as Laplacian gradient operator. In remote sensing images, because of the high variation rate, the edge detection operators may have some weaknesses in correct detection of the scope of complications. This study provides a novel approach for detecting the edges based on the features of remote sensing images. In this method, at first, thresholds of different regions of the image were determined in a piecewise manner; then, by using the proposed methods, appropriate thresholds were extracted, and finally, the boundary between these regions was extracted using Shannon entropy. The obtained results were compared with some standard algorithms and it was observed that the method was efficiently able to detect edges.