Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and ...interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future.
Despite the many conveniences of Radio Frequency Identification (RFID) systems, the underlying open architecture for communication between the RFID devices may lead to various security threats. ...Recently, many solutions were proposed to secure RFID systems and many such systems are based on only lightweight primitives, including symmetric encryption, hash functions, and exclusive OR operation. Many solutions based on only lightweight primitives were proved insecure, whereas, due to resource-constrained nature of RFID devices, the public key-based cryptographic solutions are unenviable for RFID systems. Very recently, Gope and Hwang proposed an authentication protocol for RFID systems based on only lightweight primitives and claimed their protocol can withstand all known attacks. However, as per the analysis in this article, their protocol is infeasible and is vulnerable to collision, denial-of-service (DoS), and stolen verifier attacks. This article then presents an improved realistic and lightweight authentication protocol to ensure protection against known attacks. The security of the proposed protocol is formally analyzed using Burrows Abadi-Needham (BAN) logic and under the attack model of automated security verification tool ProVerif. Moreover, the security features are also well analyzed, although informally. The proposed protocol outperforms the competing protocols in terms of security.
Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize ...analysts' efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.
The advancement in communication and computation technologies has paved a way for connecting large number of heterogeneous devices to offer specified services. Still, the advantages of this ...advancement are not realized completely due to inherent security issues. Most of the existing authentication mechanisms ensure the legitimacy of requesting user thorough single server leading towards multiple registrations and corresponding credentials storage on user side. Intelligent multimedia networks (IMN) may encompass wide range of networks and applications. However, the privacy and security of IMN cannot be apprehended through traditional multi sign on/single server authentication systems. The multi-server authentication systems can enable a user to acquire services from multiple servers using single registration and with single set of credentials (i.e.Password/smart card etc.) and can be accomplish IMN security and privacy needs. In 2018, Barman et al. proposed a multi-server authentication protocol using fuzzy commitment. The authors claimed that their protocol provides anonymity while resisting all known attacks. In this paper, we analyze that Barman et al.’s protocol is still vulnerable to anonymity violation attack and impersonation based on stolen smart card attack; moreover, it has incomplete login request and is prone to scalability issues. We then propose an enhanced protocol to overcome the security weaknesses of Barman et al.’s scheme. The security of the proposed protocol is verified using BAN logic and widely accepted automated AVISPA tool. The BAN logic and automated AVISPA along with the informal analysis ensure the robustness of the scheme against all known attacks.
Wearable computing has a great prospect in smart healthcare applications. The emergence of the Internet of Things, Wireless Body Area Networks (WBANs), and big data processing open numerous ...challenges and opportunities. In healthcare, the monitoring is done by placing/implanting sensor nodes (resource-constrained devices) on a patient’s body to communicate data to a resource-rich node called a sink. The data transmission energy consumption is directly proportional to the distance between the sensor and the sink node. Therefore, it is vital to reduce the energy consumption of the sensor node due to data transmission. In this article, a new
Dual Forwarder Selection Technique
(DFST) has been proposed to prolong the network lifetime by reducing energy consumption and ultimately improving the stability period and throughput of the network. The DFST works by grouping sensor nodes on a body where both forwarder nodes have been selected through a cost function for relaying data to the sink. The proposed scheme’s efficiency has been evaluated using simulation results in terms of network stability, lifetime, and throughput. Energy consumption of sensor nodes minimized, which, as a result, increased residual network energy. The number of dead nodes of the DFST is about 50% less than that of its counterparts RE-ATTEMPT and iM-SIMPLE. The average throughput of the proposed scheme is 51% and 8% higher than the methods. Similarly, the residual energy of the DFST is approximately 200% and 120% more than iM-SIMPLE and RE-ATTEMPT, respectively.
Internet of Vehicles (IoV) is a new emerging concept and is an extended notion of Vehicular Ad-hoc networks (VANETs). In IoV the vehicles (nodes) are connected to the internet and able to transmit ...information. However, due to resources constraint nature of vehicles, they may not want to cooperate in order to save its own resources such as memory, energy, and buffer, etc. This behavior may lead to poor system performance. IoV needs an efficient solution to motivate the nodes in terms of cooperation to avoid selfish behavior. A novel mechanism Incentive and Punishment Scheme (IPS) has been proposed in this article where vehicles with higher weight and cooperation are elected as Heads during the election process. Vickrey, Clarke, and Groves (VCG) model has been used to scrutinize the weight of these heads. Vehicle participating in the election process can increase its incentives (reputation) by active participation (forwarding data). Vehicles with repeated selfish behavior are punished. The monitoring nodes monitor the performance of their neighbor nodes after the election process. A mathematical model and algorithms has been developed for the election, monitoring and incentive processes. The proposed approach has been simulated through VDTNSim environment to analyze the performance of the proposed IPS. The performance results demonstrate that the proposed schemes outperform the existing schemes in terms of packet delivery ratio, average delivery delay, average cost, and overhead.
Finding rising stars (FRS) is a hot research topic investigated recently for diverse application domains. These days, people are more interested in finding people who will become experts shortly to ...fill junior positions than finding existing experts who can immediately fill senior positions. FRS can increase productivity wherever they join due to their vibrant and energetic behavior. In this paper, we assess the methods to find FRS. The existing methods are classified into ranking-, prediction-, clustering-, and analysis-based methods, and the pros and cons of these methods are discussed. Details of standard datasets and performance-evaluation measures are also provided for this growing area of research. We conclude by discussing open challenges and future directions in this prosperous area of research.
Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large ...scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The district-level simulated yield data set was analyzed with three ML models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>: 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.
The Internet of Things (IoT), cloud, and fog computing are now a reality and have become the vision of the smart world. Self-directed learning approaches, their tools, and smart spaces are ...transforming traditional institutions into smart institutions. This transition has a positive impact on learner engagement, motivation, attendance, and advanced learning outcomes. In developing countries, there are many barriers to quality education, such as inadequate implementation of standard operating procedures, lack of involvement from learners and parents, and lack of transparent performance measurement for both institutions and students. These issues need to be addressed to ensure further growth and improvement. This study explored the use of smart technologies (IoT, fog, and cloud computing) to address challenges in student learning and administrative tasks. A novel framework (a five-element smart institution framework) is proposed to connect administrators, teachers, parents, and students using smart technologies to improve attendance, pedagogy, and evaluation. The results showed significant increases in student attendance and homework progress, along with improvements in annual results, student discipline, and teacher/parent engagement.
Machine learning techniques have shown promising results in classifying NFR. However, the lack of annotated training data in the domain of requirement engineering poses challenges to the accuracy, ...generalization, and reliability of ML-based methods, including overfitting, poor performance, biased models, and out-of-vocabulary issues. This study presents an approach for the classification of non-functional requirements (NFRs) in software requirements specification documents by extracting features from word embedding pre-trained models. The novel algorithms are specifically designed to extract relevant representative features from pre-trained word embedding models. In addition, each pre-trained model is paired with the four tailored neural network architectures for NFR classification including RPCNN, RPBiLSTM, RPLSTM, and RPANN. This combination results in the creation of twelve unique models, each with its unique configuration and characteristics. The results show that the integration of pre-trained GloVe models with RPBiLSTM demonstrates the highest performance, achieving an impressive average Area Under the Curve (AUC) score of 96%, a precision of 85%, and recall of 82%, highlighting its strong ability to accurately classify NFRs. Furthermore, among the integration of pre-trained Word2Vec models, RPLSTM achieved notable results, with an AUC score of 95%, precision of 86%, and recall of 80%. Similarly, integrated fastText-based pre-trained models the RPBiLSTM yield competitive performance, with an AUC score of 95%, precision of 85%, and recall of 80%. This comprehensive and integrated approach provides a practical solution for effectively analyzing and classifying NFRs, thereby facilitating improved software development practices.
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