The increasing demand for ultra-reliable and low-latency communication (URLLC) in vehicle road cooperation systems (VRCS) has propelled the development of intelligent and efficient optimization techniques. This paper presents a Quantum Deep Q-Network (QDQN) based approach for minimizing latency in a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) enabled VRCS. STAR-RIS improves signal coverage and energy efficiency by simultaneously serving users in both transmission and reflection modes. However, latency optimization remains a critical challenge due to dynamic environments and computational complexity. The proposed QDQN technique integrates quantum computing principles with deep reinforcement learning (DRL) to accelerate decision making and optimize resource allocation in real time. Using quantum parallelism and entanglement, QDQN reduces convergence time while effectively learning the dynamic state of the communication environment. The simulation results demonstrate that the proposed method achieves a significant latency reduction compared to conventional DRL and classical Q-learning techniques. This study highlights the potential of quantum-enhanced reinforcement learning for future URLLC applications in intelligent vehicular networks.

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency on a single centralized controller is challenging due to the scalability and resilience issues. Thus, deploying multiple controllers becomes inevitable to process the data with maximum throughput and minimum delay. Controller placement problem (CPP) is a major issue that needs to be addressed by designing efficient solutions. To address the CPP, two parameters, i) number of controllers and ii) location of controllers, need to be handled optimally. Thus, an Optimal COntroller Placement Scheme (COPS) using the multi-objective evolutionary approach for SDN is proposed in this paper. The results prove its effectiveness in terms of various evaluation parameters.

With the advent of smart and autonomous vehicles, a number of novel data-intensive and latency-critical vehicular communication applications have emerged. However, dynamic vehicular mobility and urban environments introduce severe propagation challenges, leading to increased latency. In order to reduce latency in Vehicle Road Cooperative Systems (VRCS), this research introduces a unique architecture that combines Asynchronous Federated Learning (AFL) with Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). The proposed system leverages a Markov Decision Process (MDP)-based optimization framework to minimize latency by jointly optimizing STAR-RIS elements and offloading decisions. Our approach allows vehicles to asynchronously update global models, ensuring robust learning while adapting to dynamic network conditions. The simulation results show that the recommended strategy provides at least a 20 % reduction in latency in AFL when compared to FL.

The rapid expansion of the Industrial Internet of Things (IIoT) in vehicular networks has significantly increased the demand for energy-efficient and low-latency communication to support intelligent transportation systems. However, the associated carbon footprint poses major challenges to sustainable development. To resolve these problems, we propose an Energy-Efficient and Latency-Minimizing Simultaneously Transmitting and Reflecting-Intelligent Reflecting Surface (STAR-IRS)-Assisted Vehicle-Road Cooperative System (VRCS) within a Carbon-Aware IIoT environment, leveraging Quantized Federated Reinforcement Learning (Q-FRL). The STAR-IRS dynamically enhances signal strength and energy efficiency by adjusting transmission and reflection coefficients, ensuring robust connectivity in complex vehicular environments. Q-FRL helps adjust STAR-IRS settings in real time while reducing computational complexity through quantized decision-making. Adaptive quantized DDPG improves energy efficiency, whereas adaptive quantized DDQN minimizes latency under carbon-aware constraints. Simulation results substantiate the performance of the proposed framework, demonstrating superior energy efficiency, lower latency, and reduced carbon emissions. Specifically, the adaptive quantized DDPG (AQ-DDPG) reduces energy consumption by 31.57%, while gradient quantized DDPG (GQ-DDPG) and fixed (4-bit) DDPG improve it by 16.84% and 6.31%, respectively, compared to fixed (2-bit) DDPG. Furthermore, AQ-DDPG reduces vehicle carbon emissions by 15% and 10% compared to FQ-DDPG and GQ-DDPG, respectively.

The convergence of intelligent transportation systems and urban informatics has given rise to the deployment of connected and autonomous vehicles (CAVs) which offers the potential to enhance the safety and efficiency. However, the increasing volume of automobiles on highways causes frequent and often mismanaged multi-lane changing (MLC), coupled with inadequate trajectory planning. This results in traffic congestion and accidents, which leads to substantial societal losses. Additionally, these issues raise substantial concerns about environmental sustainability, safety, and traffic efficiency, necessitating innovative solutions. To address these challenges, we leverage the transformative capabilities of Artificial Intelligence of Things (AIoT) and introduce a deep reinforcement learning (DRL)-based non-cooperative game approach, named Nash-SAC (Soft Actor-Critic), enabled by digital twin technology, to facilitate optimized decision-making in CAVs. We consider various driving behaviors and social interaction characteristics that influence driving safety, ride comfort, and travel efficiency. The efficacy of the proposed framework is validated through simulations using the Python-based Highway-env simulator and Matlab/Simulink. The simulation analysis reveals that the proposed algorithm attains 22.48%, 40.32%, and 52.02% reductions in average delay, and achieves 39.50%, 58%, and 64.46% lesser computational time compared to the Twin-Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN) algorithms, respectively.

The battery issue with Internet of Things (IoT) devices has been identified as a feasible solution in the shape of forthcoming backscatter communication technology. Wireless sensor networks, for example, that use backscatter communication technology can effectively monitor remote situations without requiring regular battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To overcome this issue, we proposed a solution that employs several unmanned aerial vehicles (UAVs) to aid in data collection. These UAVs may approach the backscatter sensor node (BSN), activate it, and then collect data. Our goal is to lower the overall flight duration required for rechargeable UAVs after the data collection mission is completed. The simulation results show that the proposed algorithms PDQN may outperform multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) approaches.

The vehicle-road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing Quality of Service (QoS). However, there are inherent challenges, such as limited bandwidth, diverse QoS requirements, interference, and power constraints, associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement-learning (QFRL)-based augmented intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL-based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL), and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.

To promote interaction between physical IoT assets and digital services, the rapid expansion of the Internet of Things (IoT) necessitates digitizing industrial processes. Integrating digital twins into an IoT network enables real-time virtualization of physical entities, allowing for efficient real-time control, rapid maintenance, and better decision-making. Furthermore, a digital twin-enabled IoT network may generate a vast amount of data, posing storage, processing, and security difficulties. In this study, we presented a blockchain and software-defined networking (SDN) integrated framework for offering decentralized and secure data operations in IoT networks to address these concerns. To filter malicious packets, the proposed system incorporates a packet analyzer and feature extraction modules at the SDN control layer. The blockchain is then constructed using an elliptic curve point technique for authenticating IoT devices. The results reveal that, when compared to the existing model, our suggested approach performs significantly better in terms of latency and throughput.

Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.

Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known asSecGreen.SecGreenhas an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as– physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of theSecGreenis compared with the existing schemes,Stable & Scalable Link Optimization (SSLO), and Secure & Energy-Efficient Blockchain-enabled (SEEB)respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, theSecGreenscheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposedSecGreenscheme in comparison to these existing competitive schemes in the literature.

With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,11.This article is an extended version of paper entitled “Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles“ published in IEEE ICC, 20-24 May 2018, Kansas City, USADiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics.

In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely- priority scheduling, routing, and re-routing. The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

Due to its widespread popularity and usage in many applications (smart transport, energy management, e-healthcare, smart ecosystem, and so on), the Internet of Things (IoT) has become popular among end users over the last few years. However, with an exponential increase in the usage of IoT technologies, we have been witnessing an increase in the number of cyber attacks on the IoT environment. An adversary can capture the private key shared between users and devices and can launch various attacks, such as IoT ransomware, Mirai botnet, man-in-the-middle, denial of service, chosen plaintext, and chosen ciphertext. To mitigate these security attacks on the IoT environment, the traditional public key cryptographic primitives are inadequate because of their high computational and communication costs. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a promising technique for secure communication. We discuss the taxonomy of two major problems, namely, the shortest path and the closest path problems with respect to the applicability of lattice-based cryptographic primitives for IoT devices. Moreover, we also discuss various LB-PKC techniques, such as NTRU, learning with errors (LWEs), and ring-LWE (R-LWE) which are often used to solve shortest path and lattice NP-hard problems in a polynomial time. We further classify the R-LWE into three categories, namely identity-based encryption, homomorphic encryption, and secure authentication key exchange. We describe the operations and algorithms adopted in each of these encryption mechanisms. Finally, we discuss the challenges, open issues, and future directions for applying LB-PKC in the IoT environment.

Improved quality of life has lead the healthcare industry to geographically expand and support real-time services. Following this trend, a surge of healthcare monitoring devices has substantially overgrown in the global market. These devices tend to generate data in humongous quantity that need real-time analysis with seamless and secure transmission to the computing nodes. The existing computing and networking infrastructures fall short to cater the services with desirable quality of service. Hence, to overcome these challenges, the proposed work presents a comprehensive platform referred as software defined network (SDN) Assisted Framework for Edge-Cloud Interplay in Secure Healthcare Ecosystem (SAFE). The objectives of SAFE include: first, an offloading scheme to support edge-cloud interplay, second, an SDN-assisted virtualized flow management scheme, and, third, a secure Lattice-based cryptosystem. Finally, the proposed scheme is validated on different performance parameters. Additionally, a security evaluation of the designed cryptosystem is also presented. The results obtained indicate the supremacy of the designed framework.

Security has become one of major concern especially in critical applications like e-healthcare. To cater to the security needs in e-healthcare, this paper proposes a novel scheme which prevents data from unauthorized fabrication and preserves the integrity of data. The proposed scheme also removes overhead of integrity validation from user's end as this work is assigned to a trusted third party, i.e., a proxy server. For this purpose, the patient's data given by user is sent to proxy server along with user's signature where it is broken down in the form of blocks. A `tag' is then generated for each block using lightweight elliptic curve cryptography (ECC). This block-tag pair is then uploaded on the data server which is used for integrity checking. Whenever a patient's data access request is raised, the block of data is retrieved using tag value and integrity is then verified. In addition to it, a lightweight lattice-based authentication scheme is proposed in the paper to authenticate the users. The request is served only when the user is deemed authentic and there is no modification in the original data sent by the user. The effectiveness of the proposed authentication scheme has been proven by performing its analysis in terms of computation time and communication cost. Moreover, the superiority of the proposed data integrity scheme has been validated by comparing it with the traditional discrete logarithmic scheme.

In recent times, smart communities such as-smart grid, smart healthcare, and smart manufacturing units consists of large number of connected devices equipped with advanced processing and communication capabilities. The focus of these smart communities have shifted towards the use of intelligent processing and control for providing better quality of service (QoS) to the end user domain. To support this aspect, software defined networking (SDN) is being widely deployed in different domains such as-data center networks, fog/edge computing, smart grid, and vehicular networks. The variable requirements of different applications in smart communities make it necessary to deploy flexible and scalable SDN. The dynamic flow management capability of SDN has lots of potential that needs to be effectively explored in order to provide QoS guarantee for traffic generated from different smart applications. In this direction, in this paper, an ensembled scheme for QoS-aware traffic flow management in SDN is designed. The proposed scheme works in three phases: 1) a linear ordering scheme for dependency removal of the incoming packets is designed, 2) an application-specific traffic classification scheme is designed, and 3) a queue management scheme is designed for efficient scheduling of traffic flow. The proposed scheme is evaluated over an experimental setup. The results obtained shows that the proposed scheme behaves effectively with respect to different QoS parameters.

Smart grid (SG) is a modernized power grid that uses information and communication technologies for bidirectional flow of information between the power utilities and the consumers. Nowadays, the focus of SG has shifted towards intelligent processing and control of various operations in order to provide high quality of experience to the end users domain (consumers, smart devices, utility, etc). Therefore, in near future, for smooth execution of various operations in SG, high volume of data is expected to move across different inter-connected smart devices. So, to handle this challenge, a self-configurable network technology known as software-defined networking (SDN)that provides faster and dynamic forwarding of data through adaptable flow-table management is a viable solution. However, in SDN- enabled SG systems, security and privacy are major challenges that need to be handled effectively. So, in this paper, a lattice-based cryptosystem for secure communication in SG environment, called LaCSys, is presented which works in three phases. In first phase, a secure authentication between all the network communication entities based on lattice based key exchange scheme is designed using a third party auditor (TPA). In second phase, a lightweight lattice-based public-key encryption scheme is designed to provide data confidentiality and integrity. In last phase, a temporary key-based scheme for detection of suspicious activity is designed. The proposed crytosystem is evaluated and compared with existing scheme in order to prove its effectiveness.

With the evolution of Internet of Things (IoT), there has been an overwhelming increase in the number of connected devices in recent years. Due to this, generation of massive amounts of data is inevitable from these enormous number of devices in IoT environment, especially in Internet of Vehicles (IoV). In such an environment, there is a need of a paradigm shift from traditional host-centric approach to a more flexible content-centric networking approach. The existing TCP/IP-based congestion control mechanisms can not be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput which most of the existing TCP variants (Tahoe, Reno, NewReno and TCP Vegas) may not be able to provide. So, in this paper, a deep learning- based content centric data dissemination approach for IoV is presented by taking into account the mobility of vehicles and type of content shared among vehicles. The proposed scheme works in three phases: 1) In the first phase, an energy estimation scheme is designed to identify the vehicles which can participate in data dissemination. 2) In the second phase, connection probability of these vehicles is computed to identify stable and reliable connections using Weiner process model. 3) In the last phase, a convolutional neural network (CNN)-based scheme for estimating the social relationship score among vehicle-to-vehicle pairs is designed. CNN is used to identify the ideal vehicle pairs, which can share data to ensure minimum delay and high data availability. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained proves the efficacy of the proposed scheme with respect to performance metrics such as-content disseminated, energy, and social score.

In a multi-cloud environment, consumers can access multiple cloud services using a single heterogeneous computing architecture. In such an environment, multiple instances of the same cloud service and its component may be geographically dispersed. So, cloud service broker (CSB) exploits the heterogeneity of multi-cloud environment to provide high performance at a low price to its consumers. The consumer tasks are allocated to the geo-dispersed cloud service components for execution of various services. For this purpose, an optimal service components identification and task allocation are major concerns keeping in view of the heterogeneity in multi-cloud environment. For this purpose, a scheduling algorithm, which takes care of location, price, and performance is required. Therefore, in this paper, SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling in Multi-cloud Environment is proposed. SLOPE works in two phases, 1) In first phase, Bayes theorem is used to design a self-learning algorithm, to compute the conditional probability (strength) of each service component in order to select the probable rule string, and 2) a roulette wheel method is used to select an optimal scheduling policy for a given service request. SLOPE helps to identify the best possible service component from the pool of resources on the basis of dynamic factors and then schedule a service request to the selected component. Unlike most of the other existing approaches, SLOPE builds an efficient schedule for service selection. Experimental results demonstrate that SLOPE performs better in comparison to other competing schemes of its category.

Industrial Internet of things (IIoT) is an emerging technology with a large number of smart connected devices having sensing, storage, and computing capabilities. IIoT is used in a wide range of applications such as transportation, healthcare, manufacturing, and energy management in smart grids. Most of the solutions reported in the literature for secure communications are not suitable for the aforementioned applications due to the usage of traditional TCP/IP-based network infrastructure. So, to handle this challenge, in this paper, a software-defined network (SDN) enabled multi-attribute secure communication model for an IIoT environment is designed. The proposed scheme works in three phases: 1) an SDN-IIoT communication model is designed using a cuckoo-filter-based fast-forwarding scheme, 2) an attribute-based encryption scheme is presented for secure data communication, and 3) a peer entity authentication scheme using a third party authenticator, Kerberos , is also presented. The proposed scheme has been evaluated using different parameters where the results obtained prove its effectiveness in comparison to the existing solutions.