Nowadays, numerous devices are utilizing the IoT world, connecting and providing access to data and sensor measurements in vast networks of interconnected objects and devices. Considering the great communication distances that need to be covered occasionally, the LoRaWAN network was proposed as it employs Low Power (LP) and Long Range (LoRa) protocols that reduce device energy consumption while maximizing communication range. A gateway to the cloud authenticates LoRaWAN IoT devices before data transmission. This procedure begins with an unencrypted Join Request. A Join Request includes, among others, a Message Integrity Code (MIC), which is the result of encrypting the unencrypted contents of the message using an AppKey that is securely stored both in the cloud and the IoT device. However, malicious actors acting as Man-In-the-Middle (MITM) can interfere in the communication channel, reverse engineer the MIC value, and derive the AppKey. They can then initiate a Join Request that is misinterpreted as coming from a legitimate device and gain access to the communication channel. This paper introduces a novel approach that focuses on the continuous regeneration of the AppKey, necessitating frequent re-joining and re-authentication of IoT devices within the network. The suggested method, which can be added as an extra layer of security in LoRaWAN networks, uses a key rolling technique similar to the one used in automobile central locking systems, and is developed as an optimised and scalable microservice for various LoRaWAN installations and versions. Through the evaluation process, significant findings emerged, demonstrating the effectiveness of the proposed security solution in mitigating replay attacks. The system successfully prevented the server from getting flooded by malicious packets, distinguishing it from a system lacking the proposed mechanism. Remarkably, this accomplishment was made without causing any noticeable delay to the communication process. In addition, the timeframe required by the proposed mechanism to generate the new AppKey is assumed to be too short for attackers to execute a replay attack, considering the computational resources currently accessible.

A swarm of UAVs (S-UAVs) is a spontaneous wireless network composed of unmanned aerial vehicles (UAVs) cooperating together to upload data to the internet. In crisis cases such as flooding or earthquakes, one or more UAVs might be non-functional, resulting in a disconnected network. Due to the continuous changes in S-UAVs, one of the most reliable routing schemes is clustering. Clustering scheme splits the network into clusters with a cluster head and cluster members. The cluster head is responsible for all intra-cluster communication, which consists of communication between the clusters. The selection of the cluster head has a high impact on the performance of the routing scheme. This paper proposes a new clustered weighted scheme with redundancy to ensure end-to-end communication even with some non-functional UAVs. The proposed protocol has a new weighted formula for the primary cluster head selection. In addition, the proposed protocol selects the secondary cluster heads based on an elimination process. The parameters of the weighted formula are the distance, the speed, and the rewarding index. The rewarding index is calculated based on the latency. This protocol proposes the presence of a secondary cluster head that automatically replaces the primary cluster head whenever it is non-functional, thus ensuring the stability of the connection. The proposed scheme is simulated using MATLAB. The results obtained are discussed towards the end of this paper. The obtained results conclude that this is a promising protocol to decrease data loss in a crisis case scenario.

Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses’ variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher’s phase, and the support vector machine method to compute the fitness function of optimisation.

Unmanned aerial vehicles (UAVs) which are commonly known as drones are highly reliable in data transportation, especially whenever terrestrial access is unavailable due to geographical limitations. Several UAVs might collaborate to perform tasks in several applications such as rescue, military, and others. This collection of UAVs is known as a swarm of unmanned aerial vehicles. Drones can be used as aerial relays in complex communication scenarios thus establishing a reliable end-to-end transmission. Wireless Sensor Network (WSN) consists of sensor nodes and base stations. The sensors of WSN are widely dispersed in predefined geographical fields. These sensors might be used for continuous monitoring for example temperature or might be used for event triggers example whenever the temperature exceeds a certain threshold an alarm is fired. In a crisis case scenario, the base station which acts as a relay to upload the sensed data to the internet might be nonfunctional thus resulting in a disconnected network and loss of sensed data. This paper proposes the usage of a swarm of UAVs to upload data from the WSN network to the internet whenever the base station is nonfunctional. This paper will study the mathematical correlation between the parameters of this network taking into consideration the constraint to avoid collisions between the UAVs. A simulation using MATLAB studies the effect of the number of levels in the WSN, the range of communication, UAV’s velocity, and UAv’s trajectory on the network performance.

The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD’s task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time.

According to the current unprecedented pandemic, we realise that we cannot respond to every contagion novel virus as fast as possible, either by vaccination or medication. Therefore, it is paramount for the sustainable development of antiviral urban ecosystems to promote early detection, control, and prevention of an outbreak. The structure of an antivirus-based multi-generational smart-city framework could be crucial to a post-COVID-19 urban environment. Humanitarian efforts in the pandemic's framework deployed novel technological solutions based on the Internet of Things (IoT), Machine Learning, Cloud Computing and Artificial Intelligence (AI). We aim to contribute by improving real-time detection using data mining in collaboration with machine learning techniques through our research work. Initially, for detection, we propose an innovative system that could detect in real-time virus propagation based on the density of the airborne COVID-19 molecules-the proposal based on the detection through the isothermal amplification RT-Lamp [1]. We also propose real-time detection by spark-induced plasma spectroscopy during the internal airborne transmission process [17]. The novelty of this research work, called characteristic subset selection, is based on identifying irrelevant data. By deducting the unrelated information dimension, machine learning algorithms would operate more efficiently. Therefore, it optimises data mining and classification in high-dimensional medical data analysis, particularly in effectively detecting COVID-19. It can play an essential role in providing timely detection with critical attributes and high accuracy. We elaborate the teaching-learning method optimisation to achieve the optimal set of features for the detection.

Flying Ad hoc Network (FANET) is the result of Unmanned Aerial Vehicles (UAVs) that collaborate to perform tasks in several applications such as rescue, military, and others. The unmanned aerial vehicle which is also known as a drone improves routing as the line-of-sight probability is increased due to its three-dimensional movement capability. Drones can be used as aerial relays in complex communication scenarios. Routing protocols are necessary for FANETs to establish communication between UAVs. Various routing protocols have been reported for FANETs targeting to find the routing protocol with minimal overhead while establishing a reliable end-to-end transmission. Wireless Sensor Network (WSN) consists of sensor nodes and base stations. The wireless sensors collect different types of information for their surroundings. Then, this information is uploaded to the internet through the base station. In a crisis case scenario, the base station might be nonfunctional resulting in a data upload failure. This paper proposes to use the UAV as an aerial base station to compensate for the nonfunctional terrestrial base station. As delay is one of the major metrics in routing protocols this paper targets to study theoretically the transmission time of packets traveling from the sensors in the WSN to the UAV. Besides, a simulation had been carried using MATLAB to study the total transfer time with respect to several network variables such as the coverage radius, the number of levels in WSN, the angle of propagation of the UAV, and the UAV's speed.

Coronavirus disease 2019 (COVID-19) is currently the most crucial emerging virus in the world. The absence of licensed medication or vaccination leads to alternative strategies. A fundamental response plan implemented by all countries was the detection and isolation of infected cases. Contact tracing of infected citizens and testing every suspected case is a prerequisite to avoid new quarantine measures. Infected cases called ‘Orphan cases’ with no epidemiological connection are more worrying. The initial method to identify them should be knowing the probability for a citizen to be infected, given that presents specific symptoms, to be tested as a suspected case and not as random. This article proposes a cloud-based identification system that studies suspected cases to increase the likelihood that a positive result is correct. Also, it introduces an innovative solution to prevent and control the further spread of Corona-virus disease based on smartphones through the deployment of cutting-edge computing systems in the framework of a Naive Bayesian Network (NBN). Furthermore, the integration of Google Maps could provide geolocation risk assessment and early inferences to government health authorities to raise the test rates in risk- prone areas.

Over recent years the focus on the comprehensive health-care system in IoT has become increasingly important, which considers in many ways a significant concept to promote health-care. It plays a positive role in increasing the highlight of the issue of medical disadvantage that threatens the medical diagnosis. Medical image constitutes a crucial carrier of the patient's diagnosis information. It nonetheless is exposed to several kinds of noise through transmission, and storage, which leads to impeding the full diagnosis for the patient and a loss of its quality as a medical digital image. Noise is a key factor in decreasing the image quality of different sorts of medical images (X ray, CAT scan, and MRI). Many techniques have been applied for image de-noising. The Discrete wavelet transform which is regarded as the most recent and optimum technique. This paper has been presented four levels of a discrete wavelet transform for the removal of Gaussian noise from several medical images based on diverse wavelet family transforms and median filtering. The proposed method submitted admissible results with regard to removing noise from medical images. The performance evaluation of the proposed algorithm is done by measuring the values PSNR, MSD, and NC.

Breakdown or interruption of communication infrastructure is one of the most immediate and significant impacts of natural disasters. This paper is devoted to the modeling of the successful uplink in Vehicular ad-hoc networks (VANET) in disaster cases where vehicles encounter disrupted communication. The packets' size to be transmitted, the vehicles' speed, the infrastructure coverage range, the disconnected distance, and the number of copies sent have a strong influence on the probability of success for uploading the packets. A simple connectionless retransmission scheme is proposed where several copies of the same packet are transmitted to make sure that the vehicle will upload successfully at least one copy of the packet. The optimum number of retransmission and the choice of inter-frame gap required between successive copy packets are also found. In this respect, this paper studies the mentioned parameters' effect on the probability of success for the uplink and the throughput. MATLAB was used to run a simulation and validate the theoretical analysis.

VANET is a spontaneous creation of a wireless network among vehicles to exchange data. Clustering is one of the most common networking protocols for data propagation in those networks. The application of the clustering algorithm is effective in VANET because the algorithm makes it a more robust and scalable network. However, due to the high mobility of nodes, it is difficult to obtain stable clusters. One of the major challenges in clustering is the cluster head (CH) election since the CH has a critical role in data routing. For stable cluster formation in VANET, some constraints such as vehicles' velocity and vehicles' separation distance must be considered while selecting the CH. This paper proposes a new clustering algorithm based on a weighted formula for calculating the probability of cluster head selection. The weighted formula is based on three parameters: the trust, the distance, and the velocity. The trust is a newly added metric that is calculated by each vehicle and broadcasted to all neighbors. Whereas, the distance and the velocity are previously treated by other papers.

Mobile cloud computing is being accepted as the model for mobile users to ubiquitously access a shared pool of cloud computing resources, data and services on-demand. In this context, elasticity debt analytics can be harnessed as a measure for efficient scheduling of cloud resources and guarantee of quality of service requirements. This paper proposes a novel green-driven, game theoretic approach to minimizing the elasticity debt on mobile cloud-based service level, investigating the case when a task is offloaded, scheduled and executed on a mobile cloud computing system. The decision to offload a mobile device user's task on cloud affects the level of elasticity debt minimization for the provided services. The research problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to: (a) predict elasticity debt and mitigate the risk of service overutilization, (b) achieve scalability as the number of mobile device user requests for cloud resources increases or decreases accordingly, and (c) optimize cloud resource provisioning, parameterizing the current pool of active users per service. The experimental results prove the effectiveness of the equilibrium model, which allocates the mobile device user requests to high elasticity debt-level services and facilitate elasticity debt minimization for greener mobile cloud computing environments.

As network bandwidth and coverage continue to increase, the adoption rates of mobile devices are growing over time and the mobile technology is becoming increasingly industrialized. In mobile cloud marketplaces, the cloud-supported mobile services can be leased off. However, the mobile service selection may introduce technical debt (TD), which is essential to be predicted and quantified. In this context, this paper examines the incurrence of technical debt in the future when leasing cloud-based mobile services by proposing a novel quantitative model, which adopts a linear and symmetric approach as a linear growth in the number of users is predicted. The formulation of the problem is based on a cost-benefit analysis, elaborating on the potential profit that could be obtained if the number of users would be equal to the maximum value. The probability of overutilization of the selected service in the long run is also researched. Finally, a quantification tool has been developed as a proof of concept (PoC), which initiates the technical debt analysis and optimization on mobile cloud-based service level and aims to provide insights into the overutilization or underutilization of a web service when a linear increase in the number of users occurs.

This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource prediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.

The efficient support of the massive device transmission is challenging in Machine-to-Machine (M2M) communications. A large number of devices activate transmissions within a short period of time in M2M communications, which in turn cause high radio access congestions and severe wireless access medium delay. In this context, this work proposes a hybrid MAC approach, based on the well-known ALOHA protocol, and intended to be applied in 5G mobile networks. The topology changes dynamically in the proposed rate-adaptive ALOHA scheme, whereas the bandwidth of a channel is utilized effectively aiming to improve the overall performance of the M2M mobile environment. A random distribution model was exploited during the conducted experiments in order to perform a systematic evaluation of bidirectional communication within a 5G mobile environment. The major contribution of the proposed scheme is the improvement of the network throughput as multiple connections are possible. The experimental results indicate the scheme's efficiency by offering high throughput as opposed to the delay variations between packets, while the proposed scheme aims at maximizing the efficiency of resource exchange between mobile peers.

This paper studies the evaluation of the video streaming over Mobile Peer-to-Peer (MP2P) networks using Scalable Video Coding. The proposed research framework exploits the MP2P diversity characteristics where each node acts as a Mobile Peer for every neighboring node in a relay-based communicating path. Mobile Peers use a common look-up table to request video streaming resources in order to estimate the video packets transfer duration in the end-to-end path. The collaborative streaming is achieved through the data packets replication policy, which uses a bounded upper time limitation for the video packets of each layer (Use of Scalable Video). Each node/device utilizes a specified amount of capacity for both channel and storage purposes. Different scenarios have been introduced in this paper towards evaluating the video streaming policy in the MP2P system, using a real-time probabilistic Fractional Brownian Motion (FBM) and a Random Waypoint (RWP) Mobility model. In both models, nodes move according to certain probabilities, location and time. Intermittent connectivity occurs while nodes claim video streams, whereas promiscuous caching policy enables -where possible- recoverability of lost packets offering priority to packets from the base layer. The simulation results show that FBM provides better objective and subjective quality results for two video sequences resolution, in the MP2P network configuration.

This paper proposes a delay-assisted cooperative scheme for optimal TV White Spaces (TVWS) exploitation and maximum energy conservation in a 5G mobile cognitive radio (CR) network architecture. This architecture utilizes a radio spectrum broker, which administrates the process of network resources management among several 5G base-stations to support Quality of Service (QoS) provision and minimum energy consumption. The proposed scheme is based on the comparison of the delays of both the secondary nodes and the Radio Access Points, when a delay sensitive transmission is requested. The validity of the proposed scheme is verified through several experimental tests, performed under controlled simulation conditions. The performance evaluation results include the energy consumption level and the lifetime span of each wireless node, the throughput response of the system during the delay-sensitive resource exchange process, as well as quantitative measurements of the energy efficiency levels of the proposed scheme.

This paper proposes two radio resource management (RRM) algorithms for efficient QoS provisioning over an infrastructure-based cognitive radio network architecture that enables for TV White Spaces exploitation. QoS provisioning and policy management is achieved via a spectrum broker that coordinates the RRM process among LTE secondary systems, under the real time secondary spectrum market policy. The proposed RRM algorithms administrate the economics of the transactions between the spectrum broker and secondary systems, following a fixed-price and an auction-based trading process. The validity of the proposed algorithms is verified via a number of tests, carried under controlled experimental conditions (i.e. simulations), evaluating spectrum broker benefit and secondary systems service rate.

This paper proposes an energy-efficient routing scheme that enables energy conservation and efficient data flow coordination, among communication nodes with heterogeneous spectrum availability in distributed cognitive radio networks. Effective routing scheme operation, as a matter of maximum energy conservation and traffic manipulation is obtained, by utilizing backward traffic activity evaluation, developed based on a simulation scenario. This simulation scenario includes a number of secondary communication nodes, operating over television white spaces (TVWS), under “spectrum of commons” regulation policy. The validity of the proposed energy-efficient routing scheme is verified, by conducting experimental simulations and obtaining performance evaluation results. Simulation results validated its efficiency for minimizing energy consumption and maximizing resources exchange among secondary communication nodes.

The paper presents an infrastructure-based cognitive radio network architecture that enables for TV white spaces exploitation, QoS provisioning and policy management, under the real time secondary spectrum market policy. It describes the configuration of a spectrum broker that coordinates the radio resource management process (RRM) among LTE secondary systems as a matter of maximum possible TVWS utilisation and minimum frequency fragmentation, and also administrates the economics of such transactions towards maximum revenue following a fixed-price trading. The validity of the proposed architecture is verified via a number of tests carried under controlled experimental conditions (i.e. simulations) exploiting a decision-making algorithm.

The paper discusses the TV white spaces exploitation by a prototype centralised cognitive radio network architecture, under the real time secondary spectrum management scheme. Vital part of this architecture is a spectrum broker that coordinates the radio resources allocation process among secondary systems, as well as the transactions of spectrum trading following a fixed-price policy. Efficient broker operation as a matter of maximum-possible spectrum utilisation and minimum fragmentation is obtained by decision-making methods based on Backtracking, Simulated Annealing and Genetic algorithm. The validity of the proposed approach is verified via a number of experiments under controlled conditions, while its performance is evaluated against a number of secondary systems competing for TVWS exploitation, each one featuring different transmission characteristics.

This paper discusses a Distributed Network Quality of Service (NQoS) provision approach in decentralized interactive DVB-T systems (IDVB-T), enabling for scalable and fault tolerant operation. The paper describes the design and overall architecture of a regenerative IDVB-T infrastructure, where network resource allocation and service classification processes are performed at local level within each intermediate distribution node (Cell Main Node CMN), setting the basis for a CMN-to-CMN NQoS provision. Validity of the proposed approach is experimentally verified, and the test-results are compared against the aggregated and federated NQoS provision approaches, indicating similar performance but better scalability and fault-tolerant design.

The paper proposes a routing protocol that efficiently coordinates data flows among secondary systems with heterogeneous spectrum availability in an ad-hoc cognitive radio network architecture. Efficient protocol operation as a matter of maximum-possible routing paths establishments and minimum delays is obtained by a coordination mechanism, which was implemented based on a simulation scenario. The simulation scenario includes a number of secondary systems that exploit television white spaces, under the spectrum of commons regime. The validity of the research approach is verified via a number of experimental tests, conducted under controlled simulation conditions, evaluating the performance of the proposed routing protocol.

This paper presents a novel user-centric networked media architecture, where content-aware capabilities at the network layer are intimately tied-up with network-aware functions at the service's layer, towards maximum possible QoS/QoE provision. Focusing on the network segment, the paper initially presents the design of a novel virtual layer on top of the traditional network plane capable to enable content awareness, describes its operational components and functions, while elaborating on its communication with legacy infrastructures.

This paper presents a preliminary implementation of a content-aware network node as part of a Content-Aware capable network infrastructure. The proposed network node facilitates all the possible functions provided by an ordinary router or gateway while at the same time exploits flow- and content- awareness in order to identify the content carried within a flow and handle it efficiently. In this context this paper discusses the main concepts and operating principles around the flow and content awareness and presents a preliminary implementation, build around the Linux operating system.

Creation and efficient distribution of new rich media services is foreseen to play a key role in the Future Internet. Emerging FI architectures should be able to fully support and facilitate all kinds of current and future media-oriented applications. The architecture illustrated in this paper facilitates the deployment of an integrated Media Ecosystem, where all users can efficiently not only have access to, but also compose and share rich media services. This is achieved via a mesh of intelligent, media-centric home gateways (“HomeBoxes”), which utilize novel Content-Aware network overlays, developed on top of the existing transport infrastructures. This concept has been designed and is being implemented as an international pilot demonstrator in the frame of the EU-funded ICT project ALICANTE.

This paper presents a novel DVB/IP infrastructure that exploits P2P technology for optimised resource exploitation in interactive services' provision. Building upon a prototype DVB-T regenerative platform, it presents a decentralised architecture that exploits the broadcasting stream as part of the core/backbone network, providing interactive IP services to rural/urban citizens. Users access the provided IP services via intermediate communication nodes (access network), which are responsible for managing/controlling both uplink and downlink flows. Towards enhancing the scalability as well as the performance of the entire network, the paper studies the realisation of IP overlays by exploiting P2P technology, and proposes a prototype configuration for optimum resource exploitation and increased Service/Bandwidth gain both at the core and access segments. Performance evaluation experiments carried-out under real transmission/reception conditions verified the validity of the proposed architecture, besides outlining fields for future research.

The paper discusses a converged DVB/IP environment capable to provide differentiated services at a guaranteed quality. Towards this the paper presents the design, implementation and integration of QoS aware mechanisms in the DVB/IP environment in order to enhance its capability as an IP networking infrastructure, optimizing the system's for the provision of IP heterogeneous services. The capability of the proposed QoS aware DVB/IP network environment is validated through experimental tests that were conducted under real transmission/reception conditions at a prototype infrastructure that conforms to the discussed architectural design issues.

The continuing and rapid growth of the Internet has created an extremely large capacity problem to the service and content provider's networks. The increased network traffic and the absence of service priority, usually produce high network congestions, delayed data and service transmissions and lack of throughput. The effect of these factors in AW content transmission is to distort the initial content and decrease the content quality. To avoid this negative effect, service and content providers are looking for architectures that give them greater control on traffic passing through their domains and other heterogeneous networks. This document proposes an experimental core network architecture (MPLS-DiffServ) presenting a solution for content providers that want reliable and agreed level of quality, even if the network is under congestion. MPLS-DiffServ architecture, couples the DiffServ'sper hop guarantees with MPLS traffic engineering capabilities. Furthermore, content/services of different format will be distributed over MPLS-DiffServ network with different guarantee limits and delivered on a variety of user terminals..

This paper introduces and validates an experimental infrastructure of a fusion IP/DVB networking environment for the provision of triple play services at remote areas exploiting the synergy of Broadcasting, Internet and Telecommunication sectors technologies. This synergy constitutes a challenge for creating a networking platform which exploits the particularities and complementarities of these sectors for the support of ubiquitous services and always-on connectivity enabling passive rural citizens to become active Information Society participants. This paper describes important directions for exploiting the proposed unified platform alleviating the digital divide that currently exists not only among countries but also within most regions of the same country.