The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular statistical metrics for evaluating demand forecasting are reviewed and summarized. This study reveals that while machine learning and deep learning are effective for demand forecasting, model selection highly depends on the target industry, data availability, and computational resources. Therefore, this study proposes a conceptual, generic framework that maps data characteristics to appropriate model architecture classes for demand forecasting and recommends adopting scale-independent evaluation metrics. The proposed framework offers a structured pipeline and practical guidance for practitioners and researchers to design forecasting systems across diverse industries, enabling consistent comparative analysis.

Healthcare IoT devices are increasingly integral to modern life, significantly enhancing health monitoring and living standards. These devices provide healthcare professionals and patients with new capabilities for continuous patient monitoring, enabling real-time analysis and intervention. However, the widespread adoption of wearable IoT devices brings both advantages and challenges. Data captured by these devices is transmitted to web applications for review by medical professionals and patients, where it can be analyzed using algorithms to suggest treatments or generate alerts. For instance, an IoT sensor that detects a patient’s abnormal heart rate can trigger an alert, prompting timely medical response. Despite these advancements, energy consumption remains a critical concern. Collecting and transmitting patient data through HIoT devices results in substantial energy use, particularly when computations are offloaded to the cloud, a common practice that increases energy demands. Real-time data analysis further complicates this issue, requiring low latency and minimal delays. To address these challenges, fog computing offers a viable solution by reducing multi-hop data connection delays, distributing resource demands, and enhancing service flexibility. This paper evaluates a Wireless Body Area Network (WBAN) model using the NS-3 simulation program on the Ubuntu platform, incorporating both externally worn and internally implanted sensors. The model’s accuracy improved with the number of hidden layers, reaching 0.9921 with sixteen layers, though at the cost of increased computational time. Additionally, the Adaptive Battery-Aware (ABA) algorithm was compared with the traditional Battery-Reliable Low-Energy (BRLE) method. Despite a slight increase in voltage usage, the ABA algorithm demonstrated superior charge recovery efficiency, offering better management of battery resources. This study provides insights into optimizing accuracy, battery management, and energy efficiency for WBANs, while also exploring energy-saving technologies like fog and edge computing in healthcare IoT architectures. The findings highlight the importance of balancing computational efficiency, energy consumption, and real-time data processing in enhancing the performance and sustainability of HIoT devices.

Robotic Process Automation (RPA) can be used to automate highly repeatable business processes. However, it might struggle in non-deterministic situations where some sort of understanding is required. For example, it can automatically load all the students’ answers from an online examination, but it cannot mark them without the help of some kind of AI. This significantly limits its capabilities. Generative AI and applications such as ChatGPT have many potential applications as they can generate and understand natural language text, making them ideal candidates for addressing the limitations of RPA. In this paper, we propose a practical way of enhancing Robotic Process Automation using Generative AI. This work also includes a practical case study that uses Visual Studio C#, Selenium, and ChatGPT for automatically grading students’ examination attempts in the Moodle Learning Management System. The results show that the application managed to grade the students with minimal human input.

Abstract This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.

In traditional Wireless Sensor Networks (WSNs) the applications are embedded in sensor nodes, making them application-oriented, domain-specific devices. Virtualization is a promising approach to allow several applications to use the physical resources of the same deployed WSN. Given that WSN sensor nodes hold limited available energy, efficient allocation and utilization of resources become important. In this paper, we study the problem of virtual network embedding in virtualized WSNs in a static setting. Our objective is to minimize the overall energy consumption by embedding the application virtual network requests onto the physical WSN substrate network. We formulate the problem as an Integer Linear Programming (ILP), accounting for latency as the service-level agreement (SLA). We then propose our so-called E2NE heuristic to achieve a near-optimal solution in a computationally efficient manner. Our results show that embedding virtual sensor networks in a virtualized WSN leads to more energy efficiency than embedding the same networks in traditional WSN without any support for virtualization. This is largely due to the fact that more physical sensors need to be activated in traditional WSN because each physical sensor can run one and only one task at a time.

We argue in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing data. The objective is to enable the cloud to be an active platform that can help data providers reach a wider set of data consumers. This will allow these consumers to be exposed to a larger variety of data that benefits data analytic applications. To achieve this vision, we propose a novel game theoretical model, which consists of a mix of cooperative and competitive strategies. The players of the game are the data providers, cloud platform, and cloud users. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects (externalities) among the players. Simulations conducted using Amazon and google clustered data show that the proposed model improves the total surplus of involved parties in terms of cloud resources provision and monetary profits compared to the current merchant model.

One of the main challenges faced by cloud providers is the uncertainty in their workload, resulting from the high variability and the dynamic nature of clients' demands. The inability to meet those demands during peak times can lead to high service rejection rates, experienced delays, and consequently profit and reputation losses. The concept of cloud federation has been proposed as a way to address this challenge, by enabling a group of cloud providers to collaborate by dynamically combining their resources as needed, to satisfy received requests. Existing cloud federation approaches fail to consider clients' SLA requirements during the coalition formation process or provide a self-healing mechanism to deal with unexpected resources' shortage during operation. Furthermore, the state of the art approaches suffer from performance issues, such as high execution times, unstable performance, and lack of convergence to a solution in complex scenarios (e.g., requests with mixed, independent types of VMs). This paper proposes a novel social gaming based approach for coalition formation in the cloud that finds the best coalition of cloud providers to answer requests, while satisfying the clients' SLA requirements. The proposed algorithm, dubbed SLA Aware Cloud Coalition Formation algorithm (S-ACCF), leverages Irving's roommate algorithm to form a stable coalition of cloud providers, with a rapid execution time. The S-ACCF algorithm is designed to maximize the coalition's profit, while minimizing the number of participants in the coalition as well as the penalty incurred by providers who fail to offer all or some of the promised resources using a self-healing process. The S-ACCF algorithm was extensively tested using a variety of scenarios, and its performance was compared to two state of the art approaches: 1) the Optimal Cloud Federation Mechanism (OCFM) that relies on an exhaustive search of all possible solutions to find the best coalition; and 2) the Cloud Federation Formation Mechanism (CFFM) that relies on an iterative split-and-merge approach to find the best coalition. While the optimal approach (OCFM) always finds the best coalition leading to the highest collective profit, it has an exponential time complexity, thus leading to very large execution times. On the other hand, the split-and-merge approach (CFFM), which relies on random selection of sub-groups for coalition formation, suffers from instability (different results in repeated runs), high and variable execution time, and a noticeable requests' rejection rate that changes between runs. The test results show that the S-ACCF algorithm addresses the limitations of the OCFM and the CFFM algorithms, and outperforms the optimal and split-and-merge approaches in terms of execution time, individual provider payoff, and the number of providers per coalition. Furthermore, it yields higher stability and zero rejection rate, when compared to the split-and-merge approach. Indeed, our proposed approach yields an execution time that is 12 to 25 times faster than the optimal and split-and-merge approaches, which is a major advantage for real-time applications. Moreover, when compared to the two other approaches, our S-ACCF algorithm always finds the smallest coalition possible satisfying the client requirements, thus leading to the highest individual payoff for providers and lower administration overhead. Finally, unlike the split-and-merge approach, our algorithm shows a stable performance, and converges towards the optimal solution in simple and complex scenarios, thus making it suitable for production environments.

The IP Multimedia System (IMS) is an important reference service delivery platform for next generation networks and is considered as a de-facto standard for IP-based multimedia communication services. In its current design, the IMS faces important challenges in terms of scalability and elasticity, and lacks the ability to adaptively manage the network resources and dynamically dimension the network nodes based on load and demand. While Virtualized IMS deployments can partially address those limitations, IMS cloud providers still need to address challenges related to the high variability and uncertainty in their workloads, which could lead to poor QoS, high delays, and reputation and financial losses. The concept of cloud federation has been recently proposed to address such challenges. This concept enables a group of cloud providers to collaborate and form a cloud coalition that dynamically combines the participants' resources as needed, to meet variable users' demands. In this work, we propose a novel and customized IMS clouds' federation approach, that is inspired by the game theoretic roommate matching algorithm. The proposed algorithm aims at maximizing the coalition profit while minimizing the penalties incurred by violated SLAs. Our proposed solution was implemented and compared to the optimal and split-and-merge cloud federation formation approaches. The simulation results obtained show that our approach outperforms the optimal and the split-and-merge approaches in terms of execution time, while always yielding the same solution as the optimal approach in the cases tested, thus making it a strong contender for practical deployments.

Fourth-Generation (4G) mobile networks are based on Long-Term Evolution (LTE) technologies and are being deployed worldwide, while research on further evolution towards the Fifth Generation (5G) has been recently initiated. 5G will be featured with advanced network infrastructure sharing capabilities among different operators. Therefore, an open-source implementation of 4G/5G networks with this capability is crucial to enable early research in this area. The main contribution of this paper is the design and implementation of such a 4G/5G open-source testbed to investigate multioperator infrastructure sharing capabilities executed in virtual architectures. The proposed design and implementation enable the virtualization and sharing of some of the components of the LTE architecture. A testbed has been implemented and validated with intensive empirical experiments conducted to validate the suitability of virtualizing LTE components in virtual infrastructures (i.e., infrastructures with multitenancy sharing capabilities). The impact of the proposed technologies can lead to significant saving of both capital and operational costs for mobile telecommunication operators.

Wireless Sensor Networks (WSNs) are an emerging type of networks formed by a set of distributed sensor nodes that collaborate to monitor environmental and physical conditions. Mobile medical sensor devices are rapidly emerging as one promising way to monitor patient health and the quality of patient care while improving convenience to the patient and reducing the cost of care by allowing patients to spend more time out of the hospital. In the future, mobile sensors could keep track of everyday behaviors that are reflective of physical and physiological health states and predictive of future health problems. We expect that wearable, portable, and even embeddable sensors will overcome some of the challenges of existing approaches and enable long-term continuous medical monitoring for many purposes. Examples include: Outpatients with chronic medical conditions (such as diabetes); individuals seeking to change behavior (such as losing weight); physicians needing to quantify and detect behavioral aberrations for early diagnosis (such as depression); or athletes wishing to monitor their condition and performance. In this paper, we focus on adapting smartphones used by individuals for health monitoring and present a case study on the design and implementation of a context-aware wireless healthcare application that leverages the capabilities of phone sensor subsystem in tracking human health conditions. The application's detailed scenario and enhanced Android architecture for this solution is presented.

The IP Multimedia Subsystem (IMS) is one of the key components of third generation (3G) networks, while Wireless Sensor Networks (WSNs) are an emerging type of networks formed by a set of distributed sensor nodes that collaborate to monitor environmental and physical conditions. Combining the capabilities of WSNs and the IMS opens the door to a wide range of personalized and adaptive value added services for 3G users. We have previously proposed a solution for WSN/IMS integration. This solution enriches the IMS architecture with context acquisition and management components, and enables access to those capabilities via standard IMS interfaces. Wireless healthcare is one of the important application areas that can benefit from the combined IMS/WSNs capabilities. In this paper, we focus on this application area and present a case study on the design and implementation of a context-aware IMS wireless healthcare application, that leverages the capabilities of our WSN/IMS integration solution. The application's detailed scenario and IMS deployment architecture are presented and a prototype is built and tested using Ericsson's IMS simulated environment.

Network virtualization is considered as a promising way to overcome the limitations and fight the gradual ossification of the current Internet infrastructure. The network virtualization concept consists in the dynamic creation of several co-existing logical network instances (or virtual networks) over a shared physical network infrastructure. One of the challenges associated with this concept is the dynamic discovery and selection of virtual resources that can be composed to form virtual networks. To achieve that task, there is a need for a formal and expressive information model facilitating information representation and sharing between the various roles/entities involved. We have previously proposed a service-oriented hierarchical business model for virtual networking environments, as well as an architecture enabling its realization. In this paper, we build on this business model and architecture by proposing a multiservice, multi-role hierarchical information model, for virtual networking environments. Furthermore, we demonstrate the usage of this information model using a secure content distribution scenario that is realized using REST interfaces. Unlike other proposals, our integrated information model enables the fine-grained description of virtual networks and virtual networking resources, in addition to the modeling of network services and roles, and their relationships and hierarchy.

Network virtualization is a promising and technically challenging concept, which enables the dynamic creation of several co-existing logical network instances (or virtual networks) over a shared physical network infrastructure. There are several motivations behind this concept, including: cost-effective sharing of resources; customizable networking solutions; and the convergence of existing network infrastructures. We have previously proposed a new business model for virtual networking environments. In this paper, we use this model as well as concrete use cases as basis for the definition of the Open Virtual Playground - an open virtual multi-services networking architecture in which different levels of services (i.e. essential services, service enablers, service building blocks, and end-user services) offered by various players, can be dynamically discovered, used, and composed. Furthermore, a QoS-enabled VoIP service scenario is used to demonstrate the system operation and preliminary performance measurements are collected.

The IP Multimedia Subsystem (IMS) is a key component of 3G networks aimed at enabling the seamless provisioning of IP multimedia services to end users. Multiparty sessions (or conferences), which enable the conversational exchange of multimedia content between several parties, represent one of the specialized services supported by the IMS. Conferencing is the basis of a wide range of applications such as: debating; multiplayer gaming; and distance learning. In order to offer users a predictable experience and a controlled quality when using such applications, suitable QoS mechanisms must be supported in the IMS. We have previously proposed a two-party call differentiation scheme along with an architectural framework enabling its realization in the IMS. In this work, we apply our call differentiation scheme to the conferencing case, by studying the effects of call differentiation on the operation of the standard IMS conferencing architecture. This is achieved via a case study including the definition of a differentiated IMS conferencing architecture, the elaboration of various differentiated multiparty sessions scenarios, and the implementation of a proof-of-concept prototype.

The IP Multimedia Subsystem (IMS) is becoming the de-facto standard for IP-based multimedia services, while Wireless Sensor Networks (WSNs) are gaining a lot of popularity due to their ability of capturing a rich set of contextual information and their high application potential. Making the rich set of contextual information, captured by WSNs, available to the IMS can greatly enhance its service provisioning capabilities and open the door to a wide range of novel multimedia services. We have previously proposed a solution for WSNs/IMS integration. One of the key components of this solution is a WSN/IMS gateway acting as intermediary between the two networks. This paper focuses on the design and implementation of this gateway. The gateway architecture proposed encompasses the information management and support functions needed to enable IMS interaction with different types of WSNs. A prototype of this architecture is built and tested using Ericsson's IMS simulated environment. Furthermore, two applications (a pervasive game and a personalized call control application) are used to demonstrate the system operation.

Emergency sessions are the most fundamental and critical services offered by telecommunications networks. They require preferential treatment over regular sessions and this is achieved with QoS and resource management techniques. An IMS emergency service architecture has been proposed by 3GPP. However, this solution only provides preferential treatment to public-initiated emergency communications. It does not offer any special treatment to mission critical calls, PSAP callbacks, and urgent communications among citizens. Furthermore, the preferential treatment offered does not provide public-initiated emergency sessions with a prioritized access to the resources at the transport layer. This may lead to unacceptable emergency sessions' setup delays and even emergency sessions failures when there is a strong contention for transport resources. This paper tackles these issues by extending the existing 3GPP architecture. We introduce new QoS profiles for emergency sessions, and propose mechanisms for supporting them. We have also built a proof of concept prototype. Its main features are presented.

Emergency services represent one of the fundamental services offered by the IP multimedia subsystem (IMS). Currently, these services rely on a limited range of contextual information (mainly location). A richer set of contextual information could be exploited to provide enhanced emergency services and lead to more efficient emergency operations. In this paper, we propose an extension of the 3GPP IMS emergency service architecture for the provision of enhanced, context-aware emergency services. The 3GPP presence framework is leveraged and extended to realize the information management aspects of the architecture. Furthermore, a proof-of-concept prototype, showcasing an enhanced emergency service scenario, is presented.

Wireless Sensor Networks (WSN) are made up of small devices that can sense context information (e.g. space, physiology, and environment). The IP Multimedia System (IMS) aims at the convergence of Internet and cellular networks. It enables the delivery of multimedia services to end-users. Integrating the sensing capabilities of WSN in the IP multimedia subsystem will open the door to a wide range of novel multimedia services. This paper proposes a presence based architecture for the integration, focusing on how the information is conveyed from the WSN to the presence infrastructure (i.e the inbound interface). Presence is an integral part of IMS. It enables the distribution of end-user presence information (e.g. location, availability), a sub-set of context information, to interested parties, generally applications. We introduce the architecture and elaborate some of the required extensions to the 3GPP presence service. The proof of concept prototype is also described.

Service differentiation is an active topic in 3G networks, which aim at supporting a diversity of IP multimedia services (with different QoS requirements) and users (establishing sessions with different levels of importance). In this paper, we propose a network level call differentiation scheme in IMS-based 3G networks. This scheme defines three new classes of calls offering different priorities/guarantees to the user. Service differentiation is achieved at the session signaling level by relying on a context-aware resource allocation strategy. An extension to the 3GPP IMS architecture is proposed to realize the service differentiation scheme and a proof-of-concept prototype is built using SIP and COPS as implementation technologies. Unlike other signaling level schemes, the system proposed here offers flexible QoS negotiation mechanisms to the user, provides preferential treatment at the beginning and during sessions, and takes into consideration the characteristics of the multimedia, multiparty, service environment offered by 3G networks.