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Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized. This work was supported in part by the European Union through the Horizon 2020: Future Emerging Topics Call (FETOPEN), under Grant EU736876; in part by the Project VISORSURF (http://www.visorsurf.eu), Research and Innovation Programme, under Agreement 739578; and in part by the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination, and Development.

In device-to-device (D2D) communications, distributed Denial-of-Service (DDoS) attacks can be quite detrimental because it can result in network structure destruction. Towards this end, the research objective of this paper is to identify and prevent DDoS and Denial-of-Service (DoS) attacks (i.e., SYN, Slowloris) in a D2D communication environment. Specifically, by replicating a real-world scenario, we emulate SLowloris attacks in a D2D communication network and generate a D2D Network-specific Slowloris dataset. This dataset along with the CICDDoS2019 dataset was then used to train our proposed Machine learning (ML) model that aids in the detection and prevention of DDoS attacks (Slowloris and SYN) in the considered D2D framework. The whole process of how to construct an emulation network for D2D communication and test it against a variety of attacks and implementations is also demonstrated in the paper. To quantify the detection accuracy in the context of DDoS and DoS attacks, we use various ML algorithms such as Random Forest, Light GBM, XGBoost, and Ada Boost and study their performance with the aid of extensive emulation. The results collected revealed that both Slowloris and CICDDoS2019 datasets achieve greater accuracy with Random Forest. Consequently, the results compel us to develop a technique for combining the identification of DDoS and DoS attacks in binary classification Random Forests with the binary decision. The proposed technique has been evaluated and compared with other related approaches in the open literature demonstrating significant performance in terms of identification and prevention time, processing and memory resources required, and device battery consumption, without affecting the accuracy of the attack identification. Hence, we advocate that our proposed technique can be extremely beneficial in preventing DDoS and DoS attacks in a D2D communication environment, where its lifetime and capabilities are mainly associated with the resources of the D2D device (i.e., CPU, Memory, and battery life).

Fog computing based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial internet of things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this paper, a novel architectural design of an SDN based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users' satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem (MINLP) to optimize the physical resource block (PRB) allocation, the assignment of user equipments (UEs) and radio unit (RU), and the downlink transmit beamforming, by considering imperfect channel state information (CSI). To solve the intractable MINLP, we exploit the successive convex approximation (SCA) approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units (vBBUs), by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent (DD) method. Through performance analysis, we show the proposed approach provides a high users' satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

As Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) applications evolve, the need for robust protocols, capable to guarantee extended lifetime and high throughput, increases. Mobility of devices either in terms of mobile nodes or mobile sinks is a promising solution that can assist, for example, topology control and congestion mitigation. Such factors significantly contribute to the extension of the lifetime and the throughput of wireless ad-hoc networks. In this work we review and classify algorithms that introduce the characteristic of mobility in WSNs. We consider WSNs to be both a subset and a predecessor to IoT, and thus, consider that existing mobility solutions can be adapted for use in IoT. Finally, open problems and future directions are discussed that include wireless power transfer, network fault detection, and real-world/testbed evaluation of algorithms.

—Fog computing based radio access network (FogRAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial internet of things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this paper, a novel architectural design of an SDN based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sumrate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem (MINLP) to optimize the physical resource block (PRB) allocation, the assignment of user equipments (UEs) and radio unit (RU), and the downlink transmit beamforming, by considering imperfect channel state information (CSI). To solve the intractable MINLP, we exploit the successive convex approximation (SCA) approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units (vBBUs), by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent (DD) method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway

5G D2D Communication promises improvements in energy and spectral efficiency, overall system capacity, and higher data rates. However, to achieve optimum results, it is essential to select wisely the Transmission mode of the D2D Device to form clusters in the most advantageous positions in terms of Sum Rate and Power Consumption. Towards this end, this paper investigates the use of Distributed Artificial Intelligence (DAI) and innovative D2D, Machine Learning (ML) approaches (i.e., DAIS, FuzzyART, DBSCAN and MEC) to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and back-hauling links in D2D network under existing Base Station. Additionally, this paper focuses on a small number of Devices (i.e., =5 for clustering, >=50 for backhauling) as lower limits.

Security breaches are an imminent threat in the Internet of Things (IoT) as smart diversified devices are now interconnected to serve a specific application. General security guidelines may fail to prevent attacks from penetrating the network and as a result an attack may immerse in the network causing irreversible damage. Detecting the attack at an early stage can minimize the effects of the attack. Using the Support Vector Machine (SVM) supervised machine learning technique in Intrusion Detection Systems (IDS) has shown that routing layer attacks can be detected by monitoring node and network activity. The current work extends on the topic of SVM detection models, by introducing Decentralized Dedicated IDS agents placed at key positions within the network to monitor it and raise an alarm when a malicious node is within its vicinity. The detectors were trained and evaluated with three main attacks and variations of them and achieve high classification and accuracy rates.

Security in the Internet of Things (IoT) is designed to ensure timely, reliable and fully operational network. The presence of a malicious device within the network can decrease the IoT applications' effective functionality by failing to transmit the data to the intended user. Intrusion detection systems (IDS) with anomaly detection classify IoT network activity based on what is defined as normal. In this paper we present the evaluation of local Binary Logistic Regression (BLR) detection models, found in mIDS, which monitor local IoT node activity to detect routing layer attacks, such as Selective Forward and Blackhole. The BLR detection models were evaluated in both a simulator and an IoT testbed platform. Overall, our results, both in the simulator and at the testbed, have shown that for each environment to be deployed, a customised BLR model should be created and more than one performance measure should be used. In the paper we propose the use of four performance metrics to fully capture the efficacy of classification methods. Besides Precision, Recall, and Accuracy, we have chosen to include the Matthews Correlation Coefficient in our evaluation set, since it provides a more normalized view and the quality of the BLR detection models.