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.

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.

In this work the problem of energy conservation in wireless Internet of Things (IoT) devices is being addressed for machine-to-machine (M2M) communication. IoT connected devices (i.e. glasses, set-top-boxes, home appliances etc.) can affect the energy levels of the IoT ecosystem and can play an active role in the level of QoS/QoE provided to the end-users, for any service demands, on-the-move. To this end, this work proposes a novel offloading methodology that hosts a “resource-aware” recommendation scheme, which allows the efficient monitoring of energy draining applications that run in an IoT ecosystem. The proposed framework allows users to have a continuous on-demand service provision where devices can actively provide the available resources to be exploited in the IoT ecosystem. Considering the latter, this work utilises an Edge-based Computing offload mechanism in M2M communication for resource-aware recommendation. The work assesses the proposed framework in the context of (i) the offered reliability for IoT services by assistive recommendation scheme and (ii) the energy conservation for a number of devices, forming the IoT ecosystem during the offloading process.

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.

The advances in Internet of Everything (IoE) and the market-oriented cloud computing have provided opportunities to resolve the challenges caused by the Internet of Things (IoT) infrastructure virtualization, capacity planning, data storage or complexity. The volume and types of IoT data motivate the need for a data storage framework towards the integration of both structured and unstructured data. In this paper, we propose a novel game theoretic technique for efficient and dynamic storage allocation in cloud-centric IoT systems. The benefit maximization problem is formulated as a cost-benefit analysis game investigating the storage capacity currently used in the cloud. In view of each player's strategy to lease additional storage capacity, the game property is analyzed and we prove that the game always admits a pure strategy Nash equilibrium. Since the player's decision affects the level of benefit maximization, we elaborate on a cost-optimal storage allocation incentive mechanism, which scales effectively once non-linear or linear demand for storage capacity occurs, towards achieving optimal leasing conditions on cloud storage and computing capacity level. The experimental validation tests prove the effectiveness of the proposed game theoretic approach allocating the requests for more storage capacity in a cost-effective manner, which achieves to maximize the benefits.

Scatter radio achieves communication by reflection and requires low-cost and low-power RF front-ends. However, its use in wireless sensor networks (WSNs) is limited, since commercial scatter radio (e.g. RFID) offers short ranges of a few tens of meters. This work redesigns scatter radio systems and maximizes range through non-classic bistatic architectures: the carrier emitter is detached from the reader. It is shown that conventional radio receivers may show a potential 3dB performance loss, since they do not exploit the correct signal model for scatter radio links. Receivers for on-off-keying (OOK) and frequency-shift keying (FSK) that overcome the frequency offset between the carrier emitter and the reader are presented. Additionally, non-coherent designs are also offered. This work emphasizes that sensor tag design should accompany receiver design. Impact of important parameters such as the antenna structural mode are presented through bit error rate (BER) results. Experimental measurements corroborate the long-range ability of bistatic radio; ranges of up to 130 meters with 20 milliwatts of carrier power are experimentally demonstrated, with commodity software radio and no directional antennas. Therefore, bistatic scatter radio may be viewed as a key enabling technology for large-scale, low-cost and low-power WSNs.

For applications that require large numbers of wireless sensors spread in a field, backscatter radio can be utilized to minimize the monetary and energy cost of each sensor. Commercial backscatter systems such as those in radio frequency identification (RFID), utilize modulation designed for the bandwidth limited regime, and require medium access control (MAC) protocols for multiple access. High tag/sensor bitrate and monostatic reader architectures result in communication range reduction. In sharp contrast, sensing applications typically require the opposite: extended communication ranges that could be achieved with bitrate reduction and bistatic reader architectures. This work presents non-coherent frequency shift keying (FSK) for bistatic backscatter radio; FSK is appropriate for the power limited regime and also allows many RF tags/sensors to convey information to a central reader simultaneously with simple frequency division multiplexing (FDM). However, classic non-coherent FSK receivers are not directly applicable in bistatic backscatter radio. This work a) carefully derives the complete signal model for bistatic backscatter radio, b) describes the details of backscatter modulation with emphasis on FSK and its corresponding receiver, c) proposes techniques to overcome the difficulties introduced by the utilization of bistatic architectures, such as the carrier frequency offset (CFO), and d) presents bit error rate (BER) performance for the proposed receiver and carrier recovery techniques.

This work studies zero-feedback distributed beamforming; we are motivated by scenarios where the links between destination and all distributed transmitters are weak, so that no reliable communication in the form of pilot signals or feedback messages can be assumed. Furthermore, we make the problem even more challenging by assuming no specialized software/hardware for distributed carrier synchronization; we are motivated by ultra-low complexity transceivers. It is found that zero-feedback (i.e. blind), constructive, distributed signal alignment at the destination is possible; the proposed scheme exploits lack of carrier synchronization among M distributed transmitters and provides beamforming gains. Possible applications include reachback communication in low-cost sensor networks with simple (i.e. conventional, no carrier frequency/phase adjustment capability) radio transceivers.

This work evaluates opportunistic relaying in the presence of thermal noise as well as interference, when channel sensing is conducted reactively, in slow fading environments. The studied scenario employs a single gateway that provides access towards several destinations with weak links and exploits a network of intermediate relays. In sharp contrast to prior art, no inter-relay channel state information or communication is assumed, no network coding is needed, while low-complexity receivers at each destination are employed. It is shown that information can be relayed without delay, while harvesting benefits of cooperative diversity, even at the presence of interference. The participating relays are required to offer strong paths towards source and destination, while at the same time they are as "isolated" as possible from each other. From that perspective, the notion of relay "usefulness" is redefined in both noise and interference-limited environments, under opportunistic relaying.

In this work, we extend low-complexity, slow-fading opportunistic relaying in both noise and interference-limited setups in order to harvest cooperation benefits without sacrificing overall delay. In particular, we show that information can be reliably relayed without delay, at the presence of interference, while harvesting benefits of cooperative diversity due to the opportunistic protocol nature. Under interference-aware opportunistic relaying, relays become useful provided that they offer strong paths towards source and destination, while at the same time they are as "isolated" as possible from each other (e.g. when directive antennas are utilized). This work could potentially assist reduced-delay, cooperative relaying applications (e.g. wireless access) with low-complexity receivers.

Abstract Simple bibliometric indicators, such as average number of citations per publication per researcher, or the recently proposed Hirsch index (h‐index), are nowadays tracked by online repositories, including Web of Science (WOS), and often affect critical decision making. This work proposes appropriate scaling of the h‐index based on its probability distribution that is calculated for any underlying citation distribution. The proposed approach outperforms existing index estimation models that have focused on the expected value only (i.e., first moment). Furthermore, it is shown that average number of citations per publication per scientific field, total number of publications per researcher, as well as researcher's h‐index measured value, expected value, and standard deviation constitute the minimum information required for meaningful h‐index ranking campaigns; otherwise contradicting ranking results emerge. This work may potentially shed light to (current or future) large‐scale, h‐index‐based bibliometric evaluations.

Sensor collision (interference) is studied in a large network of low bit-rate sensors that communicate via backscatter, i.e. modulate the reflection of a common carrier transmitted by a central reader. Closed-form analysis is provided, quantifying sensor collision (interference) in high-density, backscatter sensor networks (BSN), as a function of number of tags and aggregate bandwidth. Analysis is applicable to a broad class of sensor subcarrier modulations, propagation environments and reader antenna directivity patterns. It is discovered that anti-collision performance in high-density backscatter sensor networks is feasible provided that appropriate modulation is used at each sensor. That is due to the round-trip nature of backscatter communication as well as the extended target range, which both impose stringent requirements on spectrum efficiency, not easily met by all modulations. Furthermore, aggregate bandwidth savings for given anti-collision performance are quantified, when simple division techniques on subcarrier (modulating) frequency and space (via moderately directive hub antenna) are combined.

In this paper, we provide low-complexity interference-aware opportunistic relaying (IaOR), suitable for reduced-delay wireless access. In particular, we show that reactive, periodic and distributed spectrum sensing by a set of cooperative relays can offer improved reliability, even in both noise and interference-limited scenarios with low-complexity receivers. This work could potentially assist reduced-delay, high node-density wireless access, as in modern urban environments.

A reconstruction algorithm for electrical impedance tomography (EIT) is presented. The least-squares (LS) method is applied and a formulation similar to that of the perturbation method is found. The main difference from perturbation lies with the sensitivity matrix, which here is replaced by the Jacobian matrix, defined in terms of the partial derivatives of every sensing electrode pair voltage difference with respect to every element's conductivity. The mutual position between the active electrodes is chosen to give optimum sensitivity. The results shown that the algorithm presented here has a better convergence and needs fewer iterations than the perturbation method.

The power gain of arrays of wire antennas near an elliptic cylinder is maximized. The hybrid MM-GTD technique is used where the method of moments (MM) and the geometrical theory of diffraction (GTD) are formally combined. Results are presented for some special cases.