Edge computing (EC) has emerged as a promising technology to meet the demand for computational resources in Internet of Things (IoT) networks. With EC, the processing of massive data-intensive tasks can occur in proximity to IoT users. Thus, required constraints related to tasks, such as latency and Quality of Service (QoS) can be guaranteed. However, determining the task offloading strategy under various constraints, including resources, distance, and cost, remains an open issue. In this article, we study the task offloading problem from a matching perspective and propose an edge-user assignment algorithm (EUAA) that aims to maximize the resource utilization of edge servers and the number of assigned IoT users. A key concern in any matching algorithm is how to generate the preference order for either side. To generate preference orders for edge servers, we apply the analytical hierarchy process (AHP), considering criteria, such as distance from users to the server, latency, resource requirements, and pricing. This approach establishes the priority of users for matching to edge servers. From the IoT users’ perspective, we use cost and QoS parameters to enhance their satisfaction. We evaluate the performance of the proposed model based on the number of assigned users, server profit, number of satisfied users, edge server resource utilization, and execution time, comparing it with state-of-the-art schemes.

The current 5G network is expected to have a densely populated architecture comprising radio-enabled Service Provider (SP) and heterogeneous User Equipment (UE). Addressing the real-time service demands of UEs with strict deadlines is a critical challenge. Unmanned Aerial Vehicle (UAV) assisted service provisioning is emerging as an efficient solution for timely service transfers. Therefore, SPs are interested in offering UAV-assisted service transmission to get profited by deploying UAVs. However, this introduces challenges like optimizing the locations of UAVs and Power Level (PL) along with interference management within limited available radio resources. Hence, we proposed a novel framework for multi-UAV-assisted service provisioning, consisting of Base Station (BS), UAVs, and heterogeneous UEs in 5G network. We formulate the SP's profit maximization problem, optimizing UAVs' location, PL, and resource allocation while considering service latency, interference management, and UAVs' energy constraints collectively as an optimization problem. Furthermore, we propose a semi-centralized sub-optimal solution utilizing Multi-agent Deep Reinforcement Learning (MaDRL) and a Graph Coloring-based approach. Extensive simulation analysis demonstrates the proposed algorithm's effectiveness, achieving an average of 99.05% profit compared to the optimal value.

In an emergency healthcare situation, delay between injury and treatment is one of the most critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by processing real-time ambulance data while en route to hospital can cut back the delay in treatment of the patient. However, several research challenges arise in accessing real-time patient data from ambulance to hospital while moving along different Road Side Units (RSUs). Due to the severity of medical data, there is a need to minimize computational losses along with costs due to migration and ambulance perceived latency. Considering the above scenarios, this paper formulates an average cost minimization problem keeping latency, energy, and loss function into deliberation as NP-hard. To solve the formulated problem, Minimum Cost Algorithm (MCA) using Federated Averaging (FedAvg) algorithm utilizing RSUs for effectively transferring real-time patient data to hospitals has been proposed considering above stated constraints altogether. Moreover, to handle imbalances in health data across different hospitals during processing, FedAvg algorithm combines augmentation techniques. Through experimental and prototype demonstration, the efficacy of proposed framework is shown by achieving$12.5 \%, 27 \%,$and$38 \%$reduction in an average total cost compared to other state-of-the-art techniques on real-world data sets, respectively.

The privacy-focused concept of Federated Learning (FL) allows local data processing without disclosing patients’ health details to a central server. However, its vulnerability to privacy breaches through shared model weights and susceptibility to a single point of failure remain concerns. Energy constraints of Wireless Body Area Networks (WBANs) necessitate considering computation and transmission energy in the FL process. Thus, this article introduces a smart healthcare system prioritizing energy efficiency and privacy through a blockchain-backed FL model. Yet, WBAN users might be unwilling to share data without adequate incentives, and miners might hesitate due to the high energy usage associated with maintaining the blockchain. Therefore, an optimization problem is formulated to maximize system utility while considering energy, WBAN incentives, miner revenue, and FL loss. A computationally efficient stable matching-based algorithm is proposed for optimizing utility via associating WBANs and miners. Associated WBANs use Quantized Neural Networks (QNNs) to minimize computation energy. Moreover, this work integrates Differential Privacy (DP) and Homomorphic Encryption (HE) mechanisms to prevent information leakage by adding noise to gradients before updating model weights and encrypting consequences before transmitting them to miners. Real-world experiments validate the framework, yielding an average of 15.1%, 9.03%, and 15.35% improvements over existing methods.

This work explores the coupling of Machine Learning (ML) and Wireless Body Area Network (WBAN) data to develop highly effective models. To support resource-constrained WBANs, we propose the integration of Drones-as-a-Service (DaaS) for on-demand data collection and model training. However, the growing number of WBAN users with varying 5G radio resources may cause interference and degrade system performance when transmitting data to Unmanned Aerial Vehicles (UAVs), hindering data sharing among independent UAVs. To address these challenges and enable privacy-preserving collaborative ML, we adopt Federated Learning (FL) framework, enabling independent UAV service providers to collaborate without sharing sensitive data. Furthermore, we aim to maximize the revenue of both WBANs, which contribute data, and UAVs, which perform model training. This requires careful resource allocation, considering minimum and maximum Physical Resource Block (PRB) requirements for transmitting critical and complete physiological data to UAVs underlying 5G networks. To tackle this complex problem, we propose an optimization framework that maximizes overall revenue while considering interference among WBANs. We apply stable matching and graph coloring-based heuristics to solve the problem efficiently. Extensive simulations and real-world data prototype demonstrate our proposed model's effectiveness, achieving an average revenue of 92.8% of the optimal value, outperforming existing state-of-the-art approaches.

Growing remote health system allows continuous monitoring of patients' conditions outside medical facilities. However, the real-time smart-healthcare applications having latency limitations, must be solved efficiently. Fog computing is emerging as an efficient solution for such real-time applications. Therefore, Medical Centers (MCs) are becoming more interested in offering IoT-based remote health monitoring services to get profited by deploying fog resources. However, an efficient algorithmic model for allocating limited fog computing resources in a criticality-aware smart-healthcare system while considering the profit of MCs is needed. Thus, we formulate an optimization problem by maximizing system utility, calculate as a linear combination of MC's profit and patients' cost together. We propose a flat-pricing based scheme to measure the profit of MC in health monitoring system. Further, we propose a swapping-based heuristic to maximize the system utility. The proposed heuristic is evaluated on various parameters and shown to be closed to the optimal while considering the criticality of patients and the profit of MC, together. Through extensive simulations, analysis on real-world data and prototype implementation, we find that the proposed heuristic achieves an average utility of 94.5% of the optimal, in polynomial time complexity.

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled Service Providers (SPs) and Service Requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs' revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching based solution. Moreover, we offer an adaptive stable matching based distributed algorithm to solve the formulated problem in a dynamic network model. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieves 94\% of optimum network performance.

Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs.1

In this article, we first formulate the joint resource allocation, interference minimization, user-level, and cell-level fairness for maximum resource reuse in 5G heterogeneous small cell networks as an NP-hard problem. We then propose three algorithms - centralized, distributed, and randomized distributed algorithms - to efficiently solve the formulated resource allocation problem while minimizing interference, maximizing fairness, and resource reuse. Through extensive real data analysis and network simulations, we show that our proposed solutions outperform state-of-the-art schemes, namely interfering model (INT) and distributed random access (DRA), for both low and high-density 5G networks.

The next-generation wireless network is expected to be highly dense with a large number of Device-to-Device (D2D) communication enabled IoT devices in fog computing based cellular networks. The dense deployment of the heterogeneous network architecture is expected to fulfill the smart devices' growing data demand, lower power consumption, and lower latency constraint. The 5G technology is expected to have such multi-tier architecture with various computational capability and radio enabled IoT devices. However, the coexistence of such heterogeneous network model spawns research challenges such as interference management, non-uniform computational capacity with non-uniform devices connectivity and service deadline. Thus, in this paper, we propose a coexistence of D2D-IoT (D-IoT) and fog computing model in cellular networks and formulate the resource allocation problem in such a multi-tier architecture considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching based solution. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieve more than 94% of optimum network performance.

Internet-of-Things (IoT) is a networking architecture where promising, intelligent services are designed via leveraging information from multiple heterogeneous sources of data within the network. However, the availability of such information in a timely manner requires processing and communication of raw data collected from these sources. Therefore, the economic feasibility of IoT-enabled networks relies on the efficient allocation of both computational and communication resources within the network. Since fog computing and 5G cellular networks approach this problem independently, there is a need for joint resource-provisioning of both communication and computational resources in the networks. As the solution to this problem, we propose a novel three-dimensional matching based resource provisioning algorithm that minimizes average service latency in the presence of various resource constraints, task deadlines and non-identical preferences at IoT devices, fog access points (FAPs) and small-cell access points (SAPs) in 5G networks. We prove the stability and termination of the proposed algorithm and also demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms through both, simulation and real-world experiments on the laboratory test-bed.

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory test-bed and comparing with the state-of-the-art algorithm.

The large number of internet-connected devices will continue to drive growth in data traffic in an exponential way, forcing network operators to increase the capacity of wireless networks. To do so in the cost-effective way a paradigm shift is occurring in 5G cellular networks from high power macro base station to small cell heterogeneous networks known as microcells, picocells, and femtocells. This paradigm shift of 5G cellular networks gives many opportunities ranging from increase capacity to reuse the scarce spectrum resources to coexistence to interference minimization etc. The coexistence of heterogeneous small cells makes the resource allocation, interference management, and maximum fairness among the users more complicated. In this paper, we formulate the resource allocation for spectrum reuse maximization, interference minimization and user level fairness in heterogeneous small cells 5G cellular networks as a NP-hard problem. We design centralized and probability based heuristic for the above resource allocation problem in-order to minimize interference and to achieve maximum spectrum reuse and fairness among the users in feasible computational complexity. We show through extensive network simulations that our proposal outperforms existing centralized interfering model (INT) and distributed random access (DRA) in both low and high-density networks.

Next-generation wireless networks are going to have highly dense, small cell structure with a large number of femtocells. The dense deployment of the femtocell network architecture is expected to meet the growing data demand by leveraging millimeter-wave structure of 5G wireless networks. However, arbitrary deployment of large number of femtocells underlying a macrocell will pose a challenge for collision and confusion-free Physical Cell ID (PCID) assignments as the total number of available PCIDs is limited to 504. In this paper we propose a distributed, randomized k-clustering algorithm for collision and confusion-free PCID assignment problem, which is known to be NP-complete. To reduce the total control message flow, we create overlapping clusters in ultra-dense femtocellular networks, where each cluster head runs the distributed randomized PCID allocation algorithm and locally monitors the conflicts to avoid the collision and confusion constraints. We prove the correctness of our proposed algorithm and analyze its time and message complexity. Through simulation experiments, we also show the effect of different parameters on the PCID allocation objectives.