Emerging megatrends like the Internet of Things (IoT), and social and mobile communication technologies are imposing new challenges on the future Internet, for which path reliability is crucial. Traditional IP networks are complex to operate and difficult to configure because they are vertically integrated: data and control plane bundled. The emerging Software-Defined Networking (SDN) architecture breaks vertical integration and separates the control logic from the data plane devices, such as switches and routers, providing a programmable infrastructure for application deployment. As a result, SDN is positioned to compute a reliable path for connected users in the event of a link failure scenario. To minimize the impact of a link failure on ongoing data flows, the prediction of a link failure requires careful attention. Machine Learning (ML) algorithms play a vital role in the link failure prediction process; however, they often have a low detection rate. To achieve a higher link failure detection rate in SDN, there is a need to design an enriched detection architecture, especially when employing ensemble ML models. This work presents LRS2, which is a meta-classification approach using base classifiers to compute a highly reliable data flow path. $\mathrm{L}\mathrm{R}\mathrm{S}^{2}$ utilizes a stacked generalization ensemble, where the base classifiers with low complexity and high diversity receive the original data as input, and each classifier predicts its subproblem. The output is a high degree of accuracy of a metaclassifier in predicting a link’s failure. Our experimental study demonstrates that the stacking ensemble has higher accuracy in predicting link failures than other ensembles or single classifiers used in the LRS2.

Artificial Intelligence of Medical Things (AIoMT) is a hybrid of the Internet of Medical Things (IoMT) and artificial intelligence to materialize the acquisition of real-time data via the smart wearable devices. Due to a diverse geographical environment of IoMT, secure, and reliable communication among these devices is a challenging task that needs to be resolved on priority basis. For this purpose, numerous device-focused authentication approaches have been proposed in the literature, however, the problem still persists. This article introduces an advanced, secured, and efficient solution for the IoMT by leveraging a lightweight mutual authentication scheme as well as facilitating AI-enabled Big Data analytics and predictive modeling. The proposed approach is specifically designed to establish secured communication between wearable sensing devices and servers within IoMT by exploiting the desirable features of cloud–edge paradigm. In this approach, every device needs to verify whether the requesting wearable device is legitimate or not and this process needs to be carried out prior to the actual communication. Our proposed approach employs a hybrid of Advanced Encryption Standard, i.e., AES-128 bit and medium access control (MAC) for the establishment of secured communication sessions. In addition, the proposed approach utilizes real-time data collection from wearable devices, enabling predictive modeling for the early detection of health anomalies, thereby, enhancing the patient outcomes of a specific disease. This continuously adaptive approach excels in real-time decision making, promptly alerting healthcare professionals of potential risks. Simulation results have verified that the proposed approach serves an ideal solution for the resource-constrained devices by achieving the expected level of authenticity through minimum possible communication and processing overhead. Additionally, this scheme is prune against well-known security attacks in the AIoMT infrastructures.

The number of applications of internet of things (IoT) devices in smart buildings keeps growing continuously, and with it, the computational tasks rendered by those devices. In smart buildings, IoT devices generate massive data traffic, and the number of devices and traffic volume increases exponentially. This issue is more sensitive in smart buildings as the management of their data is critical. Therefore, matching the task’s differential needs (e.g., energy, delay) with the network resources is paramount. In a device-to-device (D2D) aided edge computing (EC) architecture, tasks can be offloaded to the resource-rich IoT device or edge node to improve offloading efficiency and minimize energy consumption and delay. Exploiting these benefits, in this paper, we propose enhanced resource allocation and energy management in smart buildings enabled by software-defined networking and EC, as well as D2D aided end-to-end communications (REED). REED aims to minimize energy consumption and delay in a smart building by jointly optimizing resource allocation and offloading decisions. To find the near-optimal solution, we use the model-free deep reinforcement learning, i.e., deep deterministic policy gradient algorithm, because the formulated problem is a mixed-integer nonlinear optimization problem with a large dimensional continuous state and action spaces in a dynamic environment. Simulation results show that the intended REED model can perform better in terms of energy consumption and delay than the other benchmark approaches.

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose SeAC: an SDN-enabled adaptive clustering technique for SIoV. SeAC uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, SeAC improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, SeAC lowers communication and computation costs. We evaluate SeAC, and the simulation results show that for two different topologies, the adaptive approach using SeAC can produce better results in terms of a stable cluster formation.

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem calledsocial and spatial physical trade-off. This problem is also referred to as themismatchproblem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure(3-D-SIS)model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the(3-D-SIS)model, we use the real traces of social networks(INFOCOM06). The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.

The Internet-of-Things (IoT) devices, backed by resourceful fog computing, are capable of meeting the requirements of computationally-intensive tasks. However, many existing IoT applications are unable to perform well, due to different Quality-of-Service (QoS) requirements, while communicating with the fog server. Besides, constantly changing traffic demands of applications is another challenge. For example, the demand for real-time applications includes communicating over a path that is less prone to delay, and applications that offload computationally intensive tasks to the fog server need a reliable path that has a lower probability of link failure. This results in a tradeoff between conflicting objectives that are constantly evolving, i.e., minimizing end-to-end delay and maximizing the reliability of paths between IoT devices and the fog server. We propose a novel approach that takes advantage of machine learning (ML) and multiobjective optimization (MOO)-based techniques. The reliability of links is evaluated using an ML-based algorithm in an software-defined network (SDN)-enabled multihop scenario for the IoT-fog environment. By considering the two conflicting objectives, the MOO algorithm is used to find the Pareto-optimal paths. Our experimental evaluation considers two applications with different QoS requirements-a real-time application (App-1) using UDP sockets and a task offloading application (App-2) using TCP sockets. Our results show that: 1) the tradeoff between the two objectives can be optimized and 2) the SDN controller was able to make adaptive decision on-the-fly to choose the best path from the Pareto-optimal set. The App-1 communicating over the selected path finished its execution in 13% less time than communicating over the shortest path. The App-2 had 41% less packet loss using the selected path compared to using the shortest path.

The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a ${K}$ -nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.