Smart city planning is envisaged as advance technology based independent and autonomous environment enabled by optimal utilisation of resources to meet the short and long run needs of its citizens. It is therefore, preeminent area of research to improve the energy consumption as a potential solution in multi-tier 5G Heterogeneous Networks (HetNets). This article predominantly focuses on energy consumption coupled with CO2 emissions in cellular networks in the context of smart cities. We use Reinforcement Learning (RL) vertical traffic offloading algorithm to optimize energy consumption in Base Stations (BSs) and to reduce carbon footprint by applying widely accepted strategy of cell switching and traffic offloading. The algorithm relies on a macro cell and multiple small cells traffic load information to determine the cell offloading strategy in most energy efficient way while maintaining quality of service demands and fulfilling users applications. Spatio-temporal simulations are performed to determine a cell switch on/off operation and offload strategy using varying traffic conditions in control data separated architecture. The simulation results of the proposed scheme prove to achieve reasonable percentage of energy and CO2 reduction.

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years.

Purpose: Infections associated with medical devices that are caused by biofilms remain a considerable challenge for health care systems owing to their multidrug resistance patterns. Biofilms of Pseudomonas aeruginosa and Staphylococcus aureus can result in life-threatening situations which are tough to eliminate by traditional methods. Antimicrobial photodynamic inactivation (aPDT) constitutes an alternative method of killing deadly pathogens and their biofilms using reactive oxygen species (ROS). This study investigated the efficacy of enhanced in vitro aPDT of P. aeruginosa and S. aureus using malachite green conjugated to carboxyl-functionalized multi-walled carbon nanotubes (MGCNT). Both the planktonic cells and biofilms of test bacteria were demonstrated to be susceptible to the MGCNT conjugate. These MGCNT conjugates may thus be employed as a facile strategy for designing antibacterial and anti-biofilm coatings to prevent the infections associated with medical devices. Methods: Conjugation of the cationic dye malachite green to carbon nanotube was studied by UV-visible spectroscopy, high-resolution transmission electron microscopy, and Fourier transform infrared spectrometry. P. aeruginosa and S. aureus photodestruction were studied using MGCNT conjugate irradiated for 3 mins with a red laser of wavelength 660 nm and radiant exposure of 58.49 J cm −2 . Results: Upon MGCNT treatment, S. aureus and P. aeruginosa were reduced by 5.16 and 5.55 log 10 , respectively. Compared to free dye, treatment with MGCNT afforded improved phototoxicity against test bacteria, concomitant with greater ROS production. The results revealed improved biofilm inhibition, exopolysaccharide inhibition, and reduced cell viability in test bacteria treated with MGCNT conjugate. P. aeruginosa and S. aureus biofilms were considerably reduced to 60.20±2.48% and 67.59±3.53%, respectively. Enhanced relative MGCNT phototoxicity in test bacteria was confirmed using confocal laser scanning microscopy. Conclusion: The findings indicated that MGCNT conjugate could be useful to eliminate the biofilms formed on medical devices by S. aureus and P. aeruginosa.

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