The broader use of Software Defined Network (SDN) controllers creates periodic changes in topology and traffic rates at routers that adapt the network to changes in network conditions.
Thus the transient behaviour of network components, and in particular routers, is becoming of great interest.
Since standard queueing models are difficult to analyze under time-varying conditions, we propose a tractable diffusion approximation for both the transient and steady-state behaviour of a network router. In particular, the analysis provides the steady-state and transient delay and packet loss probability as a function of traffic load and other characteristics. Using these results, we show that when SDN routers change the paths of flows frequently, the network's behaviour may often be far from its steady-state behaviour. Therefore any network optimization conducted with the help of SDN should not be based on steady-state behaviour, but rather on some metric reated to the network time dependent behaviour.
In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.
01378nas a2200157 4500000000100000000000100001008004100002100002400043700001700067700001900084700001800103245008100121300001400202490000900216520099500225 2020 d 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aTime Dependent Diffusion Model for Security Driven Software Defined Networks app. 38-56 0 v2792 3 aWe present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed
to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things. Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour. Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along complete paths between senders and receivers. Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.
It has been recently observed that Software Defined Networks (SDN) can change the
paths of different connections in the network at a relatively frequent pace to improve the overall
network performance, including delay and packet loss, or to respond to other needs such as security.
These changes mean that a network that SDN controls will seldom operate in steady state; rather,
the network may often be in transient mode, especially when the network is heavily loaded and
path changes are critically important. Hence, we propose a transient analysis of such networks to
better understand how frequent changes in paths and the switches’ workloads may affect multi-hop
networks’ performance. Since conventional queueing models are difficult to solve for transient
behaviour and simulations take excessive computation time due to the need for statistical accuracy,
we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show
that network optimization should consider the transient effects of SDN and that transients need to be
included in the design of algorithms for SDN controllers that optimize network performance.
Today’s telecommunication network infrastructure and services are dramatically changing due partially to the rapid increase in the amount of traffic generation and its transportation. This rapid change is also caused by the increased demand for a high quality of services and the recent interest in green networking strengthened by cutting down carbon emission and operation cost. Access networks generate short electronic packets of different sizes, which are aggregated into larger optical packets at the ingress edge nodes of the optical backbone network. It is transported transparently in the optical domain, reconverted into the electronic domain at the egress edge nodes, and delivered to the destination access networks. Packet aggregation provides many benefits at the level of MAN, and core networks such as, increased spectral efficiency, energy efficiency, optimal resource utilisation, simplified traffic management which significantly reduces protocol and signalling overhead. However, packet aggregation introduces performance bottleneck at the edge node as the packets from the access networks are temporarily stored in the aggregation buffers during the packet aggregation process. In this article, we apply the diffusion approximation model and other stochastic modelling methods to analytically evaluate the performance of a new packet aggregation mechanism which was developed specifically for an N-GREEN (Next Generation of Routers for Energy Efficiency) metro network. We obtain the distribution of the packets’ queue in the aggregation buffer, which influences the distribution of the waiting time (delay) experienced by packets in the aggregation buffer. We then, demonstrate the influence of the probability p of successfully inserting the packet data units from the aggregation queue to the optical ring within a defined timeslot Δ. We also discuss the performance evaluation of the complete ring by deriving the utilisation of each link.
01634nas a2200145 4500000000100000000000100001008004100002260003300043100001900076700002000095700001700115700002000132245012100152520121500273 2021 d bIEEE aCape Town-South Africa 1 aGodlove Kuaban 1 aBhavneet Soodan 1 aRakesh Kumar 1 aPiotr Czekalski 00 aAnalysis of the Performance of a Cloud Computing Processing Queue with Correlated Reneging of Tasks and Resubmission 3 aThe Quality of Service (QoS) experienced by the packets that constitute a cloud computing task does not only depend on the performance of the cloud computing network infrastructure at the data centre but also on the performance of the access networks and the internet core networks traversed by the packets before the task is fully submitted to the cloud computing or processing servers for execution or processing. Sometimes, some of the tasks that are submitted and are waiting in queues to be processed could be removed from the queues without being processed (task reneging or dropping). Removing tasks from the queue could result from the user's impatience, missing execution deadline, security reasons, or an active queue management strategy. The reneged or dropped tasks could be resubmitted depending on the reason for which it was dropped. The resubmission of tasks decreases QoS and increases the energy consumption overhead as more energy is required to transport the resubmitted tasks through the access, core, and data centre networks. In this paper, we propose a multiserver queueing model with correlated reneging and resubmission to analyse the performance of a cloud computing server.
02123nas a2200157 4500000000100000000000100001008004100002100001700043700002000060700001900080700002000099700001800119245010300137490001000240520171500250 2021 d 1 aRakesh Kumar 1 aBhavneet Soodan 1 aGodlove Kuaban 1 aPiotr Czekalski 1 aSapana Sharma 00 aPerformance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging 0 v2091 3 aQueuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like a vera ge queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging
01281nas a2200157 4500000000100000000000100001008004100002260002700043100002400070700001700094700001900111700001800130245005000148490000900198520091600207 2022 d bSpringer aNice, France 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aOptimizing Energy Usage for an Electric Drone 0 v1596 3 aUnmanned Aerial Vehicles (UAVs) are rapidly gaining popu-
larity in a wide variety of applications, e.g., agriculture, health care, envi-
ronmental management, supply chains, law enforcement, surveillance,
and photography. Dones are often powered by batteries, making energy
a critical resource that must be optimised during the mission of the
drone. The duration of a done’s mission depends on the amount of energy
required to perform some manoeuvering actions (takeoff, level flight, hov-
ering, and landing), the energy required to power the ICT modules in
the drone, the drone’s speed, payload, and the wind. In this paper, we
present a model that minimizes the energy consumption of a low power
drone and maximizes the time required to completely drain the drone’s
battery and ensure the safe landing of the drone.
The complexity of battery-powered autonomous de-
vices such as Internet of Things (IoT) nodes or Unmanned Aerial
Vehicles (UAV) and the necessity to ensure an acceptable quality
of service, reliability, and security, have significantly increased
their energy demand. In this paper, we discuss using a diffusion
approximation process to approximate the dynamic changes in
the energy content of a battery. We consider the case when
energy harvesting sources are constantly charging the battery.
The model assumes a probabilistic consumption and delivery of
energy, giving the time-dependent distributions of the energy at
the battery, of the time remaining until it becomes empty, the
time required to charge the battery to its total capacity, or the
time it is operational between two moments of complete depletion.
When possible, we compare the diffusion approximation results
with corresponding Markovian models.
The complexity of battery-powered autonomous devices such as Internet of Things (IoT) nodes or Unmanned Aerial Vehicles (UAV) and the necessity to ensure an acceptable quality of service, reliability, and security have significantly increased their energy demand. In this paper, we discuss using a diffusion approximation process to approximate the dynamic changes in the energy content of a battery. We consider the case when energy harvesting sources are constantly charging the battery. The model assumes a probabilistic consumption and delivery of energy, giving the time-dependent distributions of the energy at the battery, of the time remaining until it becomes empty, the
time required to charge the battery to its total capacity, or the time it is operational between two moments of complete depletion.
When possible, we compare the diffusion approximation results with corresponding Markovian models.
Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate Δλ for a fixed service rate μ) results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking.
a978-1-6654-7095-7 02195nas a2200217 4500000000100000000000100001008004100002260003300043100001900076700001800095700002000113700002100133700001800154700002300172700002000195700002000215700001500235245011200250520159300362020002201955 2022 d bIEEE aPrague, Czech Republic 1 aGodlove Kuaban 1 aMateusz Nowak 1 aPiotr Czekalski 1 aKrzysztof Tokarz 1 aJulius Tangka 1 aKjartan Siggursson 1 aAgris Nikitenko 1 aKarlis Berkolds 1 aRaivo Sell 00 aAn IoT Course Program to Foster the Adoption of IoT Driven Food and Agriculture in Sub-Saharan Africa (SSA) 3 aThe Internet of Things (IoT) technologies, together with other technological advances such as big data analytics, Artificial Intelligence (AI), automation, and unmanned electrical vehicles (e.g., self-driving vehicles and drones), are currently being used to transform the food and agriculture industry to increase yields significantly with minimal damage to the environment. These recent advances in Information and Communication Technologies and advances in biotechnology are driving the transition into the fourth agricultural revolution or agriculture 4.0. Some research studies on the adoption of IoT in the agriculture sector in Sub-Saharan Africa concluded that some of the reasons the rate of adoption of IoT in Africa is still very low are gaps in knowledge, skills, finance, and infrastructure. The knowledge and skills gaps are partly a result of an educational curriculum that is not up to date with current advances in the food and agricultural industry. To foster IoT adoption in the food and agriculture sectors and other sectors or industries in Sub-saharan Africa, we propose an IoT course program consisting of lectures and laboratory modules. The course is accompanied by textbooks, video lectures that are freely available on Edx, and remote online laboratories with the hardware resources hosted in some European countries but accessible through the Internet. These courses can also be adapted by vocational training schools, Higher Institutes, and universities in Sub-Saharan Africa or could be used by professionals and enthusiasts to improve their skills.
a978-1-6654-7087-2 01276nas a2200193 4500000000100000000000100001000000100002008004100003260002700044100002400071700001700095700001900112700001800131245005000149300001200199490000900211520084000220020002201060 2022 d bSpringer aNice, France 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aOptimizing Energy Usage for an Electric Drone a61–75 0 v1596 3 aUnmanned Aerial Vehicles (UAVs) are rapidly gaining popularity in a wide variety of applications, e.g., agriculture, health care, environmental management, supply chains, law enforcement, surveillance, and photography. Dones are often powered by batteries, making energy
a critical resource that must be optimised during the mission of the drone. The duration of a done’s mission depends on the amount of energy required to perform some manoeuvering actions (takeoff, level flight, hovering, and landing), the energy required to power the ICT modules in the drone, the drone’s speed, payload, and the wind. In this paper, we present a model that minimizes the energy consumption of a low-power drone and maximizes the time required to completely drain the drone’s battery and ensure the safe landing of the drone.
Customers are often required to wait when they arrive at service facilities and see the servers are busy, or when they find other customers who arrived earlier waiting. The longer customers wait, the more dissatisfied they are likely to be and may leave the queue without receiving service (reneging). The objective of the service provider is to improve the quality of service in order to minimise the possibility of customer reneging since it increases cost and reduces revenue. Therefore, a trade-off between performance and cost should be considered when designing, planning and reducing the queues at service facilities. In this paper, we propose an adaptive queuing model with the retention of reneging customers. We derive the steady-state and transient-state performance parameters, and also discuss performance and cost evaluation. We demonstrate the utility of the model in the evaluation of waiting lines in the service industry using numerical examples.02144nas a2200169 4500000000100000000000100001008004100002260001200043100001900055700001700074700002400091700002000115700001800135245012900153490000700282520168500289 2023 d c07/2023 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 1 aJulius Tangka 00 aModelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices 0 v23 3 a
The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.01628nas a2200181 4500000000100000000000100001008004100002260000900043100001900052700002000071700001900091700001500110700002000125700002000145700001700165245012300182520114100305 2023 d bIEEE 1 aGodlove Kuaban 1 aPiotr Czekalski 1 aOnyeka Nwobodo 1 aRaivo Sell 1 aAgris Nikitenko 1 aKarlis Berkolds 1 aKenedy Tanko 00 aSmart Sustainable Agrivoltaics Systems: The Future of Sustainable Agricultural Technology (Agri-tech) and Green Energy 3 a
Agricultural productivity depends primarily on energy, water, and land resources, which are increasingly becoming more scarce and expensive. Electricity generation with photovoltaic (PV) solar energy technology requires significant amounts of space, especially in densely populated countries, generating a societal debate about allocating land (that may have alternative uses) for deploying PV systems. Rather than dedicating vast amounts of agricultural land to be used as solar farms, PV systems are deployed in agricultural lands so that a given piece of land can be used for agriculture and energy generation (the so-called agrivoltaics). A framework based on systems thinking is essential for the design and operation of smart, sustainable agrivoltaics systems to meet the design goals or to satisfy the expectation of all stakeholders (farmers, energy developers, policymakers, and local community members that may be impacted). In this paper, we propose a system-based conceptual and design framework for smart, sustainable agrivoltaics. We also discuss the benefits of agrivoltaics and the challenges to their adoption.
01685nas a2200145 4500000000100000000000100001008004100002260004300043100001900086700001700105700002400122700002000146245009000166520128300256 2024 d c16/01/2024 bIEEE aStony Brook, NY, USA 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 00 aModelling the Energy Performance of Off-Grid Sustainable Green Cellular Base Stations 3 aThere is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers. We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period.01666nas a2200169 4500000000100000000000100001008004100002260000900043100001900052700002400071700001700095700001800112700002000130245009500150520122900245020002201474 2024 d bIEEE 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aSapana Sharma 1 aPiotr Czekalski 00 aA Markov model for a Self-Powered Green IoT Device with State-Dependent Energy Consumption 3 a
The growing concerns about the resource sustainability and environmental impact of deploying billions or trillions of IoT devices in the various sectors of society or economy have given rise to an energy research field known as Green-IoT (G-IoT). G-IoT emphasises the need to prioritise energy efficiency and the reduction of pollution in the IoT ecosystem. This paper presents a Markov model for a self-powered Green IoT device with state-dependent energy consumption. The model evaluates the energy performance of a self-powered Green IoT device. We derive the probability distribution of the energy content of the battery, including the probability that the energy stored in the battery is completely depleted and the density of the lifetime of the IoT device. We investigate the influence of some design parameters on the results. Introducing the threshold at which the device is forced to enter an energy-saving regime significantly increases a G-IoT device's lifetime. Apart from deploying energy-efficient mechanisms to increase the lifetime of a G-IoT device, integrating an efficient energy harvesting system into the device will increase its lifetime and reduce the frequency of battery replacement.a979-8-3503-3691-7 02099nas a2200181 4500000000100000000000100001008004100002260004000043100001900083700002400102700001700126700001600143700001900159700002000178245008400198520161300282020002201895 2024 d c17/07/2024 bIEEE aAyia Napa, Cyprus 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aValery Nkemeni 1 aPiotr Czekalski 00 aEnergy performance of Internet of Things (IoT) networks for pipeline monitoring 3 a
Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (especially water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery's energy content and to estimate the mean and variance of the device's lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node's lifetime.a979-8-3503-6126-1 01486nas a2200169 4500000000100000000000100001008004100002260001500043100001900058700001700077700002400094700002000118700001900138245006400157490000800221520108700229 2024 d c10/07/2024 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 1 aValery Nkemeni 00 aEnergy Performance of Off-Grid Green Cellular Base Stations 0 v165 3 a
The most energy-hungry parts of mobile networks are the base station sites, which consume around $60-80\%$ of their total energy. One of the approaches for relieving this energy pressure on the electricity grid infrastructure and reducing the Operational Expenditures (OPEX) is to power base stations with renewable energy. However, the design of a green mobile network requires the dimensioning of the energy harvesting and storage systems through the estimation of the network's energy demand. Therefore, this paper develops a diffusion-based modelling framework for solar-powered green off-grid base station sites. We apply this framework to evaluate the energy performance of homogeneous and hybrid energy storage systems supplied by harvested solar energy. We present the complete analysis, with numerical examples, to study the relationship between the design parameters and the energy performance metrics. The numerical computations demonstrate how the proposed framework can be applied to evaluate homogeneous and unconventional hybrid energy storage systems.01871nas a2200157 4500000000100000000000100001008004100002100001900043700001900062700002000081700001000101700001900111700002000130245010700150520145600257 2024 d 1 aOnyeka Nwobodo 1 aGodlove Kuaban 1 aTomasz Kukuczka 1 aKamil 1 aWereszczyński 1 aKrzysztof Cyran 00 aAnalysis of Marker and SLAM-based Tracking for Advanced Augmented Reality (AR)-based Flight Simulation 3 a
Augmented reality (AR)-based flight simulation reshapes how pilots are trained, offering an immersive environment where commercial
and fighter pilots can be trained at low cost with minimal use of fuel and safety concerns. This study conducts a pioneering comparative analysis of marker-based tracking and SLAM technologies within the Microsoft HoloLens 2 platform, mainly focusing on their efficacy in landing manoeuvre simulations. Our investigation incorporates an experimental setup where marker-based tracking overlays interactive video tutorials onto a simulated cockpit, enhancing the realism and effectiveness of landing procedures. The experiment demonstrates that marker-based systems ensure high precision within 5 cm and 15 cm from the HoloLens 2 camera, proving indispensable for procedural training that requires exact overlay precision. Conversely, the native SLAM algorithm, while lacking the same level of precision, offers flexibility and adaptability by accurately mapping the cockpit and superimposing virtual information in dynamic, markerless conditions. The study juxtaposes these technologies, revealing a trade-off between precision and adaptability, and suggests an integrative approach to leverage their respective strengths. Our findings provide pivotal insights for developers and training institutions to optimize AR flight simulation training, contributing to advanced, immersive pilot training programs.
Augmented reality (AR)-based flight simulation reshapes how pilots are trained, offering an immersive environment where commercial
and fighter pilots can be trained at low cost with minimal use of fuel and safety concerns. This study conducts a pioneering comparative analysis of marker-based tracking and SLAM technologies within the Microsoft HoloLens 2 platform, mainly focusing on their efficacy in landing manoeuvre simulations. Our investigation incorporates an experimental setup where marker-based tracking overlays interactive video tutorials onto a simulated cockpit, enhancing the realism and effectiveness of landing procedures. The experiment demonstrates that marker-based systems ensure high precision within 5 cm and 15 cm from the HoloLens 2 camera, proving indispensable for procedural training that requires exact overlay precision. Conversely, the native SLAM algorithm, while lacking the same level of precision, offers flexibility and adaptability by accurately mapping the cockpit and superimposing virtual information in dynamic, markerless conditions. The study juxtaposes these technologies, revealing a trade-off between precision and adaptability, and suggests an integrative approach to leverage their respective strengths. Our findings provide pivotal insights for developers and training institutions to optimize AR flight simulation training, contributing to advanced, immersive pilot training programs.
There is a growing desire by mobile network operators and other stakeholders to reduce carbon emissions from the operation of mobile networks. The widespread adoption of ultra-dense 5G and Internet of Things (IoT) networks will likely increase the energy demand from these networks significantly. In this paper, we model the density of the time required to charge BESS to its full capacity when the renewable energy sources can generate enough energy to meet the needs of the base station site and the density of the time required to completely deplete the energy stored in BESS when the renewable energy sources cannot generate a sufficient amount of energy to meet the demand of the site. We also investigate the influence of the design parameters, such as the energy supply-demand ratio, on the distribution of the time required to charge BESS to its full capacity (for a supply-demand ratio greater than one) and the time required to completely deplete the energy stored in BESS (for supply-demand ratio less than one).a978-99916-55-80-2 01952nas a2200169 4500000000100000000000100001008004100002260001500043100001900058700001900077700001900096700002000115700002200135245012100157490000700278520149700285 2024 d c16/10/2024 1 aGodlove Kuaban 1 aValery Nkemeni 1 aOnyeka Nwobodo 1 aPiotr Czekalski 1 aFabien Mieyeville 00 aInternet of Things Adoption in Technology Ecosystems within the Central African Region: The Case of Silicon Mountain 0 v16 3 a
The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors and industries worldwide. Despite its global significance, the adoption and implementation of IoT technologies in emerging technology ecosystems within the Central African region still need to be studied and explored. This paper presents a case study of the Silicon Mountain technology ecosystem, located in the Fako division of the southwest region of Cameroon, focusing on the barriers and challenges to adopting and integrating IoT technologies within this emerging tech ecosystem. Through a survey research method, we investigate the factors influencing IoT adoption in the Silicon Mountain tech ecosystem, including technological, economic, social, and regulatory factors. Our study reveals key insights into the current state of IoT adoption, opportunities for growth and innovation, and IoT adoption challenges. Key among the challenges identified for impeding IoT uptake were issues related to standardization and financial resources, labor shortage in the industry, educational and knowledge gaps, market challenges, government policies, security and data privacy concerns, and inadequate power supply. Based on our findings, we provide recommendations for policymakers, industry stakeholders, and academic institutions to promote and facilitate the widespread adoption of IoT technologies in Silicon Mountain and the Central African region at large.
02260nas a2200205 4500000000100000000000100001008004100002260001500043100001900058700002400077700001700101700001600118700001800134700001800152700001900170700002000189245005500209490000700264520178300271 2024 d c07/11/2024 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aSapana Sharma 1 aPradeep Singh 1 aValery Nkemeni 1 aPiotr Czekalski 00 aEnergy performance of self-powered Green IoT nodes 0 v12 3 aThe widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the Energy Storage Systems (ESS) is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). This paper investigates the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimising the probability of service outage and energy wastage).
01664nas a2200181 4500000000100000000000100001008004100002260002500043100001900068700002400087700001700111700001600128700001900144700002000163245007400183856005100257520117400308 2024 d bIEEE aKrakow, Poland 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aValery Nkemeni 1 aPiotr Czekalski 00 aImpact of energy leakage on the energy performance of green IoT nodes uhttps://ieeexplore.ieee.org/document/10786519 3 aIn the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).
01577nas a2200193 4500000000100000000000100001008004100002260001300043100001600056700001900072700001900091700001400110700001900124700001800143700001900161700002200180245010000202520108100302 d bSpringer 1 aMangeh Jaja 1 aValery Nkemeni 1 aGodlove Kuaban 1 aAkum Acha 1 aOnyeka Nwobodo 1 aEric Djomadji 1 aPierre Tsafack 1 aPierre Brosselard 00 aA Comprehensive Crop Recommendation System Lever- aging Internet of Things and Machine Learning 3 aIn the pursuit of sustainable agriculture and food security, the efficient use of soil nutrients is paramount. This paper presents the development of an Internet of Things (IoT) and Machine Learning (ML)-based system for crop recommendation. The system features a sensor that leverages visible infrared spectroscopy to accurately determine the nitrogen-phosphorus-potassium (NPK) concentration in soil. Integrated into a sensor node comprising of the ESP32 system-on-chip (SoC), this device collects real-time soil data and transmits it to the cloud. In the cloud, a web application employs a machine learning model developed from the random forest ML algorithm to analyze the NPK data and recommend the most suitable crops for cultivation on the given land. The developed model achieved a 91.1% overall accuracy. This approach not only optimizes crop selection but also promotes sustainable agricultural practices by ensuring that crops are matched to the soil's nutrient profile, consequently leading to improved crop yield while reducing environmental impact.
01697nas a2200157 4500000000100000000000100001008004100002260001300043100001900056700001900075700002400094700002000118700001800138245008300156520130000239 d bSpringer 1 aGodlove Kuaban 1 aValery Nkemeni 1 aTadeusz Czachórski 1 aPiotr Czekalski 1 aEric Djomadji 00 aDimensioning the renewable energy system of a Green Cellular Base Station Site 3 aThe demand for mobile communication services has experienced astronomical growth in the past few decades and continues to grow steadily at a brisk rate. This is due partly to falling prices of user devices, the evolution of mobile applications, the widespread adoption of social media, and the recent advances in modern technologies such as the Internet of Things (IoT), cloud/fog computing, and Artificial Intelligence (AI). An expansion of mobile communication infrastructure to accommodate the rapidly growing demand and to migrate to new technologies (e.g., 4G/5G) have also resulted in a significant increase in the energy consumption of mobile networks. An attractive solution to reduce the carbon footprint of mobile networks and to reduce the energy bills of network operators is to deploy renewable energy systems(e.g., solar energy systems) to power base station sites. In this paper, we present a practical approach for dimensioning and evaluating renewable generation and storage systems for cellular base station sites. We use data collected from a base station site in Banda (North region of Cameroon) as input to the analytical model; we estimate the probability of service outage (when all the stored energy is consumed and the PV system is not able to generate energy).
01547nas a2200229 4500000000100000000000100001008004100002260001500043100001900058700002200077700001900099700002000118700002100138700002000159700001800179700001800197700001900215700002700234245009800261490000700359520095100366 2024 d c31/10/2024 1 aValery Nkemeni 1 aFabien Mieyeville 1 aGodlove Kuaban 1 aPiotr Czekalski 1 aKrzysztof Tokarz 1 aWirnkar Nsanyuy 1 aEric Djomadji 1 aMusong Katche 1 aPierre Tsafack 1 aBartłomiej Zieliński 00 aEvaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks 0 v24 3 aBattery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, net-
work energy balancing, and energy replenishment, this study assesses their effectiveness when im- plemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.
There is growing widespread adoption of augmented reality (AR) in tech-driven industries and sectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling, entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantum computing. However, ensuring smooth and intuitive interactions with augmented objects is challenging, requiring practical performance evaluation and optimization models to assess and improve users’ experiences with AR-enhanced systems. In this paper, we apply Fitts’ Law to model and predict interaction task difficulty with objects distributed across four spatial quadrants. We use genetic optimization algorithms to fine-tune Fitts’s Law parameters, achieving a model that significantly enhances predictive accuracy. Our optimized model demonstrates an approximately 40% reduction in interaction task difficulty across all quadrants, leading to a more ergonomic and intuitive user interface. This study contributes to the Human-Computer Interaction (HCI) field by offering a refined metric for evaluating and optimizing AR interfaces and addressing the unique challenges of three-dimensional interaction environments. Therefore, we propose a practical framework for the performance evaluations and optimization of augmented reality and other user interfaces.
a2169-3536 00376nas a2200133 4500000000100000000000100001008004100002100001900043700001900062700001900081700002500100700002000125245009700145 2025 d 1 aOnyeka Nwobodo 1 aGodlove Kuaban 1 aValery Nkemeni 1 aKamil Wereszczyński 1 aKRZYSZTOF CYRAN 00 aA Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators 01796nas a2200109 4500000000100000000000100001008004100002260003700043100001900080245008200099520150500181 2025 d c02/07/2025 bIEEE aAbu Dhabi, UAE 1 aGodlove Kuaban 00 aAn Analytical Design Framework for Dimensioning Solar-powered Green IoT Nodes 3 aThe rapid adoption of the Internet of Things (IoT) across various industries has significantly enhanced productivity and efficiency. However, the widespread deployment of battery-powered IoT devices raises concerns about energy sustainability, battery longevity, and environmental impact. Frequent battery replacements pose logistical challenges, particularly in large-scale networks and remote locations. To address these issues, the Green IoT (G-IoT) design framework has been introduced, emphasizing energy-efficient strategies such as duty cycling, transceiver optimization, energy-aware routing, and the integration of renewable energy sources like photovoltaic (PV) energy harvesting. Despite advancements in G-IoT, a major challenge remains: the dynamic and unpredictable nature of both IoT energy consumption and harvested energy. In this paper, we propose a design framework for Green IoT that models the interplay between energy harvesting, storage, and consumption processes. By incorporating time-varying energy dynamics, the proposed framework enables efficient dimensioning of energy harvesters and storage systems, ensuring optimal energy management in IoT networks. This approach allows for the evaluation of various energy-saving strategies and enhances the long-term sustainability of IoT deployments. Numerical examples and simulation results demonstrate the effectiveness of the framework in maintaining energy balance and extending the operational lifetime of IoT nodes.
02128nas a2200157 4500000000100000000000100001008004100002260003900043100001900082700001900101700002500120700002000145245012700165520165600292020002201948 2025 d c07/2025 bSpringer, Cham aSingapore 1 aOnyeka Nwobodo 1 aGodlove Kuaban 1 aKamil Wereszczyński 1 aKRZYSZTOF CYRAN 00 aEnhancing Learning in Augmented Reality (AR): A Deep Learning Framework for Predicting Memory Retention in AR Environments 3 aThe integration of Artificial Intelligence (AI) with Augmented Reality (AR) has transformed human-computer interaction, offering new opportunities for immersive learning and cognitive assessment. However, the relationship between user engagement in AR environments and memory retention remains underexplored. This study proposes an AI-driven framework for predicting memory retention using behavioural interaction data captured through Microsoft HoloLens 2 sensors. The model estimates the likelihood of object recall in AR-based learning environments by analyzing key interaction metrics such as gaze duration, interaction frequency, revisit counts, and head movement stability.
To validate the AI predictions, we compared model-generated retention scores with user-reported recall, demonstrating a strong alignment between predicted and actual memory performance. Our findings align with established cognitive theories, indicating that increased interaction and attentional engagement enhance memory retention. Furthermore, comparisons with prior research on perceptual judgments and spatial memory reinforce the model’s effectiveness in capturing real-world cognitive processes. This study introduces a scalable, non-invasive approach to cognitive modeling, bridging AI-driven analytics with AR-based learning. The results have broad implications for education, medical training, AR-based flight simulation training, and workforce development, where optimizing learning efficiency is crucial. By leveraging AI for real-time memory prediction, this research paves the way for more adaptive and personalized AR learning experiences.
The transportation sector is responsible for approximately 23% of global greenhouse gas (GHG) emissions, with road transportation contributing nearly 70% of these emissions. The widespread adoption of electric vehicles (EVs) is transforming this sector by reducing emissions and decreasing reliance on fossil fuels. However, the growing number of EVs presents significant challenges for charging infrastructure, particularly in managing long queues, extended wait times, and limited station capacity. Most existing studies on the performance of electric vehicle charging stations assume Poisson arrivals and exponential charging times, simplifications that often overlook real-world variability. This paper introduces a generalized queueing model that leverages empirical interarrival and charging duration data for more accurate performance evaluation. A transient analysis is conducted, examining two operational optimization strategies aimed at minimizing queue sizes during peak demand: (1) a queue management policy that encourages charging only up to a predefined state-of-charge (SoC) threshold instead of the typical 80–100%, and (2) dynamic control of the number of active charging ports based on demand. The results show that these operational optimization policies improve the efficiency of the charging station and significantly improve the customer experience.
a978-3-031-97635-3 01362nas a2200205 4500000000100000000000100001008004100002260001500043100001500058700002000073700002100093700001900114700002000133700002000153700001700173245007000190490000700260520087500267022001401142 2025 d c07/05/2025 1 aRaivo Sell 1 aPiotr Czekalski 1 aKrzysztof Tokarz 1 aGodlove Kuaban 1 aAgris Nikitenko 1 aKarlis Berkolds 1 aLukasz Lipka 00 aAn international hands-on IoT education platform towards Industry 0 v74 3 aThis paper presents VREL NextGen, an international Internet of Things (IoT) education platform developed through European university collaboration. The platform addresses hands-on IoT education needs in the Industry 5.0 context through three distinct implementations: a microcontroller laboratory at the Silesian University of Technology, a building management sensor network at Riga Technical University, and a human-robot collaboration platform at Tallinn University of Technology. The system enables remote access to physical hardware while promoting resource sharing and standardized educational approaches. Implementation results demonstrate effective integration of theoretical knowledge with practical skills, supporting both fundamental engineering concepts and advanced IoT applications while emphasizing human-centric design principles of Industry 5.0
a1736-7530 01966nas a2200157 4500000000100000000000100001008004100002260001500043100001900058700001900077700002000096245012100116856004600237490000700283520151800290 2025 d c15/05/2025 1 aGodlove Kuaban 1 aValery Nkemeni 1 aPiotr Czekalski 00 aAn Analytical Framework for Optimizing the Renewable Energy Dimensioning of Green IoT Systems in Pipeline Monitoring uhttps://www.mdpi.com/1424-8220/25/10/3137 0 v25 3 aThe increasing demand for sustainable and autonomous monitoring solutions in critical infrastructure has driven interest in Green Internet of Things (G-IoT) systems. This paper presents an analytical and experimental framework for designing energy-efficient, self-sustaining pipeline monitoring systems that leverage renewable energy harvesting and low-power operation techniques. We propose a hybrid approach combining solar energy harvesting with energy-saving strategies such as adaptive sensing, duty cycling, and distributed computing to extend the lifetime of IoT nodes without human intervention. Using real-world irradiance data and energy profiling from a prototype testbed, we analyze the impact of solar panel sizing, energy storage capacity, energy-saving strategies, and energy leakage on the energy balance of IoT nodes. The simulation results show that, with optimal dimensioning, harvested solar energy can sustain pipeline monitoring operations over multi-year periods, even under variable environmental conditions. We investigated the influence of design parameters such as duty cycling, solar panel area, the capacity of the energy storage system, and the energy leakage coefficient on energy performance metrics such as the autonomy or lifetime of the node (time required to drain all the stored energy), which is an important design object. This framework provides practical design insights for the scalable deployment of G-IoT systems in energy-constrained outdoor environments. 02899nas a2200181 4500000000100000000000100001008004100002260002700043100002000070700002100090700001900111700001500130700002000145700002000165700001800185245008700203520242700290 2025 d bElsevier aOsaka, Japan 1 aPiotr Czekalski 1 aKrzysztof Tokarz 1 aGodlove Kuaban 1 aRaivo Sell 1 aAgris Nikitenko 1 aKarlis Berkolds 1 aŁukasz Lipka 00 aDesign and Implementation of a Next-Generation Remote Lab for IoT and Industry 4.0 3 aThe rapid adoption of the Internet of Things (IoT) across various industries, including manufacturing, healthcare, transportation, energy, and smart cities, has created a significant demand for skilled professionals in IoT hardware design, software development, deployment, and maintenance. However, the educational landscape has struggled to keep pace with this demand, as many universities lack dedicated IoT programs or fail to integrate IoT education into non-IT disciplines. This gap has led to a shortage of professionals skilled in IoT, posing a challenge for industries seeking to harness IoT technologies effectively. To address this challenge, next-generation remote laboratory infrastructures have emerged as a viable solution, enabling hands-on learning with IoT devices without the constraints of physical access to hardware. These infrastructures are particularly valuable for lifelong learners, students in remote areas, and institutions with limited resources, offering scalable and flexible access to IoT experimentation platforms via the Internet. Furthermore, the COVID-19 pandemic underscored the necessity of resilient and accessible online learning solutions, reinforcing the role of Virtual and Remote E-Laboratory (VREL) platforms in modern education. This paper presents the design, implementation, and operation of a next-generation remote laboratory infrastructure for IoT and Industry 4.0 education and research. Our approach builds upon the outcomes of two European Commission-funded projects—IOT-OPEN.EU (2016-2019) and IOT-OPEN.EU Reloaded (2022-2025) focuses on providing scalable, secure, and accessible remote IoT education solutions. We outline the architectural framework, key components, and technical challenges of deploying such infrastructures, including connectivity, security, scalability, and user management. Additionally, we discuss the educational impact of these platforms and their role in addressing the global IoT skills gap. Our findings demonstrate that remote IoT laboratories can be an effective and scalable alternative to traditional hands-on learning, supporting academic institutions and industry professionals in acquiring practical IoT skills. These infrastructures can enhance IoT education and workforce readiness by leveraging open-source technologies and modular architectures, ensuring broader accessibility and fostering innovation in the field.
01024nas a2200145 4500000000100000000000100001008004100002260002700043100002000070700002500090700002500115700001900140245007400159520064500233 2025 d bElsevier aOsaka, Japan 1 aPiotr Czekalski 1 aMirosław Borowiecki 1 aBolesław Pochopie´ 1 aGodlove Kuaban 00 aAn Analysis and Implementation of the Switching Sequence Table Method 3 a
This paper details the algorithm implementation of the Switching Sequence Table method in asynchronous sequential circuit Boolean switching system design. The algorithm is theoretically analysed in terms of its computational complexity and validated through actual experiments. It discusses the multidimensional problem of various parameters and their impact on performance. It then presents analytical evidence that reducing this multidimensional problem to a standard, one-dimensional problem is feasible. The authors consider method challenges when implementing computing modules for the Switching Sequence Table method.
The Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency; however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitors powering IoT devices. The emergence of Green IoT (G-IoT) frameworks, which prioritize energy efficiency and renewable energy integration, offers a promising solution to address this challenge. Despite these advancements, energy storage systems (ESSs) face issues such as capacity degradation, leakage, and charge redistribution, which can lead to energy depletion and service disruptions. Traditional models often assume constant energy harvesting rates, overlooking the time-varying nature of environmental conditions that influence energy availability. In this paper, we propose a novel mathematical framework that incorporates time-dependent fluctuations in the energy harvesting rate to analyse the dynamic interactions between energy harvesting, leakage, and consumption in green IoT systems. Our approach includes an Energy Packet Network (EPN) model to represent transient energy dynamics and a Markov model to capture fluctuations in the energy harvesting rate. Through numerical simulations, we evaluate the impact of key design parameters, such as ESS capacity, mean energy consumption rate, energy leakage, and energy harvesting rate variations, on critical performance metrics including the probability of energy depletion and the transient mean number of stored energy packets. The results highlight the importance of considering time-varying energy harvesting in the design and optimization of IoT systems for long-term operation and sustainability.
02041nas a2200157 4500000000100000000000100001008004100002260004100043100001900084700001700103700001800120700002400138700002000162245010800182520159300290 2025 d c02/12/2025 bIEEE aMarrakech, Morocco 1 aGodlove Kuaban 1 aTulin Atmaca 1 aZeynep Turgut 1 aTadeusz Czachórski 1 aPiotr Czekalski 00 aLeveraging Packet Aggregation to Optimise QoS and Energy Trade-offs in 5G/6G Optical Fronthaul Networks 3 aThe growing demands of 5G and emerging 6G mobile networks—driven by the proliferation of IoT devices, immersive applications, and high-throughput services—necessitate more efficient and scalable fronthaul solutions. Open Radio Access Networks and cloud-RAN architectures offer promising frameworks by decoupling hardware and software and enabling centralised processing. However, the fronthaul segment still faces challenges related to bandwidth inefficiency, signalling overhead, and relatively high energy consumption, particularly due to the transmission of vast numbers of small packets. Packet aggregation emerges as a viable strategy to mitigate these challenges by grouping multiple small packets into larger payloads, thereby reducing protocol overhead, improving throughput efficiency, and lowering energy consumption in the network as a whole. This paper investigates the impact of packet aggregation on both quality of service (QoS) and energy efficiency in the fronthaul of 5G/6G mobile networks. While prior studies have examined its effects on throughput and latency, the implications for energy consumption remain underexplored. We evaluate how aggregation design parameters—such as time thresholds, size thresholds, and traffic intensity—influence performance metrics such as throughput and latency. We also propose ways to achieve an optimal trade-off between QoS and energy efficiency. Our findings offer insights into the design of energy-aware, delay-sensitive packet aggregation schemes tailored for next-generation mobile network architectures.
02212nas a2200157 4500000000100000000000100001008004100002260003300043100001900076700002400095700001700119700001600136700002000152245011000172520177200282 2025 d c12/2025 bIEEE aParis, France 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aPiotr Czekalski 00 aEnergy Dynamics of Green IoT Nodes with Time-Varying Energy Harvesting, Leakage, and Consumption Patterns 3 aThe growing proliferation of Internet of Things (IoT) devices has intensified the need for sustainable energy solutions, particularly in resource-constrained deployments where non-rechargeable batteries and supercapacitors are the primary energy sources. Green IoT (G-IoT) frameworks address this challenge by combining energy-saving techniques with energy harvesting from ambient sources such as solar power. However, the intermittent nature of renewable energy and the non-ideal behavior of energy storage systems—such as energy leakage and capacity degradation—complicate reliable energy provisioning. This paper presents a novel Markovian framework for modelling the coupled dynamics of time-varying solar energy harvesting, time-dependent energy consumption, and state-dependent energy leakage in G-IoT systems. Unlike traditional steady-state models, our approach uses Discrete-Time Markov Chains (DTMCs) to capture the stochastic variability in both energy harvesting and consumption processes. We also introduce a refined leakage model in which the leakage rate is dynamically dependent on the stored energy level, enabling a more realistic characterization of energy losses due to energy leakage. Through extensive analytical evaluation, we examine how key parameters—such as storage capacity, leakage rate coefficient, and energy harvesting and consumption patterns—affect critical performance metrics, including the mean stored energy and energy-related service outage probability. Furthermore, we propose a parameter tuning strategy to optimize energy reliability and storage efficiency. The proposed model provides valuable insights for the design and optimization of robust, energy-aware IoT systems powered by renewable energy sources.
01648nas a2200145 4500000000100000000000100001008004100002260003300043100001900076700002400095700001700119700002000136245007700156520126900233 2025 d c12/2025 bIEEE aParis, France 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aTulin Atmaca 1 aPiotr Czekalski 00 aAnalytical Power Efficiency Models for Open Radio Access Network (O-RAN) 3 aThe rapid deployment of 5G and the anticipated evolution to 6G have intensified the need for energy-efficient radio access networks, driven by rapidly growing power demands, rising energy costs, and environmental concerns. Open Radio Access Networks (O-RAN) offer a flexible and scalable architecture; however, their disaggregated nature requires accurate power modelling to optimise energy consumption. This paper proposes enhanced power consumption models for O-RAN that account for power efficiency gains from resource sharing across multiple radio units (O-RUs) and the offloading of highly parallel baseband processing tasks to specialised energy-efficient hardware accelerators such as ASICs, FPGAs, or GPUs. The models are used to analyse the scaling behavior of O-RUs, distributed units (O-DUs), and centralized units (O-CUs) under varying deployment scenarios. Results show that O-RUs dominate the total network power budget, while O-DUs benefit from sublinear growth due to statistical multiplexing, and O-CUs scale the slowest owing to centralised control-plane sharing. Furthermore, the study identifies optimal ranges of RU-to-DU aggregation and accelerator utilization for maximising energy efficiency in large-scale 5G/6G O-RAN deployments.
01617nas a2200145 4500000000100000000000100001008004100002260002900043100001900072700002400091700001700115700002000132245005800152520126100210 d bIEEE aLas Vegas, NV, USA 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aTulin Atmaca 1 aPiotr Czekalski 00 aPower models for O-RAN O-DU planning and optimisation 3 aThe transition from 5G to 6G networks is driving an unprecedented densification of Radio Access Networks (RANs), resulting in increased energy demand and operational costs. Open RAN (O-RAN) architectures, which disaggregate baseband processing across commercial off-the-shelf (COTS) servers, further amplify these challenges due to the power sensitivity of the Distributed Unit (O-DU) to traffic load variations. This paper develops a nonlinear O-DU power model that captures load-dependent efficiency effects and integrates it into a dynamic core management framework, which activates or deactivates CPU cores based on instantaneous traffic demand. The effectiveness of the proposed dynamic core management framework is investigated in multi-RU O-RAN scenarios, comparing the proposed dynamic approach with a static baseline. Simulation results show that the dynamic scheme reduces energy consumption by up to $59.42\%$ on average under light traffic, with instantaneous savings exceeding $70\%$ during off-peak periods, while maintaining performance. These findings demonstrate that workload-aware core provisioning can deliver substantial energy efficiency gains in O-RAN deployments, particularly in lightly to moderately loaded networks.
02493nas a2200145 4500000000100000000000100001008004100002260001700043100001500060700001900075700001700094700001900111245013600130520208100266 2025 d c 25-Nov-2025 1 aKinge Osee 1 aValery Nkemeni 1 aMichael Sone 1 aGodlove Kuaban 00 aA Hybrid Ensemble-Based QoE Prediction from QoS and User Satisfaction Data: A case study on Cameroon’s 3G/4G Mobile Networks 3 aThe rapid growth of mobile internet and social media usage in Sub-Saharan Africa has amplified the need for accurate Quality of Experience (QoE) assessment in resource-constrained network environments. This paper introduces a novel hybrid Machine Learning (ML) framework for predicting user QoE in Cameroon’s Third Generation and Fourth Generation (3G/4G) networks, leveraging a unique real-world dataset that integrates crowdsourced Quality of Service (QoS) measurements with subjective user satisfaction surveys. Addressing the limitations of existing QoE studies that focus on well-resourced regions, our approach proposes QoE Predictor (QoEPredict). This stacking ensemble combines eXtreme Gradient Boosting (XGBoost) and Random Forest classifiers with an XGBoost meta-learner. A key innovation is the use of disagreement features to capture divergences between base model predictions, allowing the meta-learner to resolve conflicts and enhance predictive accuracy. The proposed five-stage pipeline incorporates data preprocessing, feature engineering via Uniform Manifold Approximation and Projection (UMAP), unsupervised clustering, and Bayesian hyperparameter optimisation using Hyperopt, ensuring a robust and transferable methodology. Explainable AI (XAI) is integrated through SHapley Additive exPlanations (SHAP) analysis to provide feature-level interpretability and actionable insights for network operators. An experimental evaluation of 1,934 user sessions demonstrates that QoEPredict achieves a 90% F1 Score and accuracy, outperforming single-model baselines across all metrics. This work represents one of the first large-scale, interpretable QoE prediction frameworks for mobile social media applications in Sub-Saharan Africa. By combining ensemble modelling with explainability and contextualised insights, the study offers both methodological advances and practical guidance for implementing QoE-aware network management strategies in developing regions facing infrastructural and operational constraints.