As mobile robots navigate through a warehouse collecting items from storage locations and transporting them to designated drop-off points, they consume energy. In this paper, we introduce an intelligent Robot Load Sharing (RLS) algorithm designed to minimize energy usage during item transportation within a warehouse by a fleet of mobile robots, ensuring sustained energy levels along their routes. The energy consumption at each time slot is parameterized as a quadratic function of the load and the traveling distance for a team of robots. A load-sharing framework is presented where pairs of off-loading and on-loading robots are selected that decide on their meeting points and the load amount to be shared. Our algorithm is compared with different no-load-sharing scenarios and we observe a significant reduction in the total energy consumption of the warehouse.

Abstract Access control of data that are outsourced to cloud storage is a challenging problem because data owners lose direct control over outsourced data. Attribute‐based encryption (ABE) is a potential cryptographic solution to provide confidentiality and flexible sharing of these outsourced data. However, the traditional ABE schemes do not meet the need of the current dynamic environment where data access not only considers the user's static and inherent attributes but also takes the user's contextual information such as location and time of access. This paper presents an improved ABE scheme using blockchain technology that can handle the frequently changing location and time attributes of data users efficiently, leading to support for fine‐grained access control of cloud storage embedding contextual information in the access policy of ABE. A prototype implementation of the proposed ABE scheme using solidity on the Ethereum platform and the experimental evaluation in terms of performance and execution cost shows a promising result.

In the past decade, the demand for Electric Vehicle (EV) charging has increased, leading to an irregular fluctuation in EV inflow at the Charging Stations (CS). The policy-makers, therefore, need to have an infrastructure planning mechanism that addresses this fluctuation by estimating the ideal location and capacity of these CSs. This problem is known as the Charging Station Siting and Sizing Problem (CSSSP). Due to the uncertainty in EV inflow, the possible non-availability of charging ports, and the resulting unpredictability in the queueing time, a limited number of EVs can get charged. To minimize this dissatisfaction with charging, we model the uncertainty in the EV demand in terms of a statistical distribution varying over time. We propose a queueing mechanism that accounts for the demand distribution over time but restricts the waiting time by a given threshold. With this mechanism, we propose an iterative heuristic algorithm where an initial allocation of ports is obtained using a policy. Then a statistical approximation approach is proposed to estimate the total unsatisfied EVs within a CS for the given port allocation as a derived random variable. Finally, an approach is proposed to intelligently rearrange the charging ports across the CSs, resulting in a fresh allocation. We repeat the steps until there is no further reduction in the unsatisfied demands. We validate the performance of the proposed method against the Monte Carlo simulation and provide a comparative analysis.

In recent years, with the support of appropriate smart contract-based access control policies, blockchain technology has proven to be a compelling solution for providing a unified, trusted platform for resource sharing. However, due to the immutability property of blockchain, it can be challenging to patch or fix bugs after the deployment of a smart contract. Therefore, it is critical to ensure that the smart contract access control policies adhere to all specifications prior to their deployment, and the presence of anomalies within policies is not desirable. Thus, this paper proposes an automated policy verification and enforcement framework for Ethereum decentralized applications, which supports an automated off-chain policy verification before its deployment to the underlying blockchain. We integrate a verification engine within the off-chain module paired with a translator to convert XACML policies into Solidity smart contracts and vice-versa. Additionally, the experiments performed on benchmark XACML policies further reinforce the pragmatism of our proposal.

The rapid growth of Internet of Things and communication technology has provided a lot of scope for the improvement of the current healthcare services. Mobile healthcare system can be used to monitor patients’ real-time physiological information and can also help in detecting and providing emergency services to the patients. For early detection of emergency, two associated requirements are - patients’ physiological details to be received in regular intervals and the data should be processed efficiently in an automated way. Additionally, patients sensitive healthcare details need to be protected also. For his purpose, this paper proposes a blockchain-enabled emergency detection system that detects emergency conditions from patients’ encrypted physiological information without decryption and automatically triggers quick responses to provide healthcare services to the patients’ via nearby hospitals. The support of blockchain technology increases accountability, transparency, and trust concerning the storage, safeguarding, and sharing of patients’ data. We present a proof of concept of our proposal using Hyperledger Fabric, and we perform an experimental evaluation using Caliper benchmarking tool to demonstrate the performance of the system.

Due to the rapid spread of COVID-19 as a global pandemic, it has become increasingly critical to have fast, cheap, and reliable tools to assist physicians in diagnosing COVID19. Several automated systems using deep learning techniques have demonstrated promising results by analyzing Computed Tomography (CT-scan) or X-ray data to complement conventional diagnostic tools. In this paper, we aim to emphasize the role of point-of-care ultrasound imaging using deep learning as a tool to detect COVID-19 more prominently. Ultrasound imaging is non-invasive and widely available in medical facilities all over the world. This paper presents an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. It classifies lung ultrasound (LUS) images of patients into COVID-19 and Non-COVID-19 categories. The lack of COVID-19 data makes it challenging to train a traditional CNN from scratch, so we have adapted a transfer learning approach instead of training the base classifiers VGG16, ResNet-50, and GoogLeNet. We apply the gained knowledge in the target domain of small lung ultrasound frames, considering the ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework is evaluated on a publicly available dataset, achieving 96.7% accuracy. The proposed architecture outperforms the state-of-the-art method on the same dataset and proves to be a reliable COVID-19 detector.

The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model.

Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this article, we study the challenging problem of optimal sizing and efficient routing for an electric vehicle-on-demand system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using mixed-integer linear programming formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms’ performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200–360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios.

Name ambiguity is a prevalent problem in scholarly publications due to the unprecedented growth of digital libraries and number of researchers. An author is identified by their name in the absence of a unique identifier. The documents of an author are mistakenly assigned due to underlying ambiguity, which may lead to an improper assessment of the author. Various efforts have been made in the literature to solve the name disambiguation problem with supervised and unsupervised approaches. The unsupervised approaches for author name disambiguation are preferred due to the availability of a large amount of unlabeled data. Bibliographic data contain heterogeneous features, thus recently, representation learning-based techniques have been used in literature to embed heterogeneous features in common space. Documents of a scholar are connected by multiple relations. Recently, research has shifted from a single homogeneous relation to multi-dimensional (heterogeneous) relations for the latent representation of document. Connections in graphs are sparse, and higher order links between documents give an additional clue. Therefore, we have used multiple neighborhoods in different relation types in heterogeneous graph for representation of documents. However, different order neighborhood in each relation type has different importance which we have empirically validated also. Therefore, to properly utilize the different neighborhoods in relation type and importance of each relation type in the heterogeneous graph, we propose attention-based multi-dimensional multi-hop neighborhood-based graph convolution network for embedding that uses the two levels of an attention, namely, (i) relation level and (ii) neighborhood level, in each relation. A significant improvement over existing state-of-the-art methods in terms of various evaluation matrices has been obtained by the proposed approach.

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge-response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge-response protocol is safe to be used in the public area network. Following this, we propose a challenge-response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method's security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).

Author name disambiguation (AND) is a challenging problem due to several issues such as missing key identifiers, same name corresponding to multiple authors, along with inconsistent representation. Several techniques have been proposed but maintaining consistent accuracy levels over all data sets is still a major challenge. We identify two major issues associated with the AND problem. First, the namesake problem in which two or more authors with the same name publishes in a similar domain. Second, the diverse topic problem in which one author publishes in diverse topical domains with a different set of coauthors. In this work, we initially propose a method named ATGEP for AND that addresses the namesake issue. We evaluate the performance of ATGEP using various ambiguous name references collected from the Arnetminer Citation (AC) and Web of Science (WoS) data set. We empirically show that the two aforementioned problems are crucial to address the AND problem that are difficult to handle using state‐of‐the‐art techniques. To handle the diverse topic issue, we extend ATGEP to a new variant named ATGEP‐web that considers external web information of the authors. Experiments show that with enough information available from external web sources ATGEP‐web can significantly improve the results further compared with ATGEP.

Entity matching aims at mapping records to different entities where records share the common entity name. The entity matching problem is challenging because several times records do not contain complete information (many of the attributes are missing), unequal distribution of records for different entities, big overlaps between records of different entities. In this paper, we have proposed an unsupervised framework to solve this problem. The aforementioned problem is posed as a partitioning problem. Three unknown artifacts for solving this partitioning problem: optimal partitioning including the optimal number of partitions, suitable distance measure which can be utilized to measure the distance between records and a set of attributes/features which can take part in the distance calculation, are determined automatically using the search capability of a multiobjective optimization technique. Several objective functions which help in measuring the goodness of partitioning are optimized simultaneously by some popular multiobjective optimization techniques, NSGA-II/NSGA-III (Non-dominated Sorting Genetic Algorithm-II/III) to solve the partitioning problem. Total 247 combinations of eight different objective functions are used in the experiments for partitioning fourteen bibliographic datasets. A detailed comparative study of the proposed approach using NSGA-II and NSGA-III.

Unaided authentication services provide the flexibility to login without being dependent on any external hardware. n‐Session recording attack resilient unaided authentication services (n‐SRRUASs) are known for setting high security standards against different client side threats. However, because of their authentication procedure, the authors have identified that these services cope poorly with handling the server side issues. Though modern days’ research heavily depends on the honeywords (or fake passwords) as a countermeasure of server side threats, they have shown that the honeywords cannot be directly applied to n‐SRRUAS. The authors’ analysis shows that the idea of incorporating the honeywords directly into an n‐SRRUAS is particularly difficult as it prevents the system from storing passwords after applying password‐based key derivation function or in the form of a hashed string. In this study, they have proposed few generic principles for incorporating the honeywords into n‐SRRUAS and show that the proposed principles are sufficient for incorporating the honeywords into any n‐SRRUAS. Furthermore, with the help of an existing n‐SRRUAS, they have shown that the proposed idea is truly implementable in practice to fill the existing gap.

The current paper aims to improve an efficient algorithm for solving the problem of non-dominated sorting which is one of the dominant steps of any Pareto based multi/many objective optimization algorithms. Recent years witnessed a large number of attempts in developing some solution frameworks for the problem of non-dominated sorting. One such recent approach is `Best Order Sort' which is efficient with respect to the number of dominance comparisons. However, this approach does not perform well in the presence of duplicate solutions. In this paper attempts have been made to modify the `Best Order Sort' and we call this modified version as `Modified Best Order Sort' to remove the above mentioned limitation. The modified best order sort algorithm has been thoroughly analyzed in different scenarios. Current work shows that `Best Order Sort' can be generalized without affecting its best and the worst case time complexities.

In this work, we propose a multi-objective optimal scheduling strategy for air-conditioning devices to optimize both energy consumption and thermal comfort for the users. We propose a graph-based modeling for the problem and utilize Johnson's all elementary circuit finding algorithm to obtain the desired solutions. The proposed methodology has been experimented on test cases that mimic real-world scenario, and further, the applicability of Karp's minimum mean cycle algorithm is also studied in this problem set-up.

Cluster validity indices are proposed in the literature to measure the goodness of a clustering result. The validity measure provides a value which shows how good or bad the obtained clustering result is, as compared to the actual clustering result. However, the validity measures are not arbitrarily generated. A validity measure should satisfy some of the important properties. However, there are cases when in-spite of satisfying these properties, a validity measure is not able to differentiate the two clustering results correctly. In this regard, sensitivity as a property of validity measure is introduced to capture the differences between the two clustering results. However, sensitivity computation is a computationally expensive task as it requires to explore all the possible combinations of clustering results which are very large in number and these are growing exponentially. So, it is required to compute the sensitivity efficiently. As the possible combinations of clustering results grow exponentially, so it is required to first obtain an upper bound on this possible number of combinations which will be sufficient to compute the value of the sensitivity. In this paper, we obtain an upper bound on the number of possible combinations of clustering results. For this purpose, a generic approach which is suitable for various validity measures and a specific approach which is applicable for two validity measures are proposed. It is also shown that this upper bound is sufficient to compute the sensitivity of various validity measures. This upper bound is very less as compared to the total number of possible combinations of clustering results.

Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand–supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the solutions are non-dominated with respect to each other. A single solution is selected by the decision maker according to his/her preference. But when the number of non-dominated solutions is large in number, then it is difficult for the decision maker to choose the one solution. The selection of a solution from the given Pareto front is known as Post-Pareto optimality analysis. In the past many approaches were proposed for solving the aforementioned problem, but most of these involve the decision maker. In this paper, we have proposed an approach to obtain a single solution from a set of non-dominated solutions by combining these solutions without the intervention of the decision maker. We have evaluated our approach on the set of solutions obtained after application of a newly developed multiobjective based clustering technique on bibliographic databases like DBLP.

Residential, commercial, and industrial buildings have been reported to consume a large portion of the generated energy. With the introduction of smart grid and its energy optimization techniques, it is now possible to efficiently manage and control consumers’ energy usage to fulfil their demands with the existing energy generation infrastructure, which otherwise seems to be a backbreaking challenge. This paper presents an efficient energy management solution for buildings with a large number of thermostatic devices (air conditioners) that maintain the temperature of different thermal zones in a predefined range. The primary objective of this paper is to schedule the thermostatic devices in order to reduce total energy consumption by these devices when they are in operation for a very long duration of time, while maintaining the other constraints. We formulate it as a graph problem where minimum mean cycle will provide the desired solution. The proposed methodology ensures that at no point in time the power consumption goes beyond a certain peak power consumption limit. We also enhance the methodology to reduce peak load consumption. Furthermore, a fast greedy approach has been developed to efficiently scale up the aforementioned scheduling scheme for a large number of devices. Experimental results show that significant improvements can be obtained by the proposed approaches over existing algorithms in reducing average energy consumption.

The phenomenal growth in the healthcare data has inspired us in investigating robust and scalable models for data mining. For classification problems Information Gain(IG) based Decision Tree is one of the popular choices. However, depending upon the nature of the dataset, IG based Decision Tree may not always perform well as it prefers the attribute with more number of distinct values as the splitting attribute. Healthcare datasets generally have many attributes and each attribute generally has many distinct values. In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG based approach, this CR based approach has no biasness towards the attribute with more number of distinct values. We have applied our model on some benchmark healthcare datasets to show the effectiveness of the proposed technique.

Entity matching is to map the records to the corresponding entity. It is a well known problem studied by many researchers over the last few years. In bibliographic database, the data evolve over time. For example, the email id of an author in DBLP and ArnetMiner which are two bibliographic databases changes with time. Authors also keep on changing their affiliations. The set of authors with whom they work also changes with time. These types of variations make the entity matching task more difficult. In this paper, we have addressed this problem and proposed the nondominated sorting genetic algorithm-II (NSGA-II) based solution framework. The dissimilarities between different records can be measured using various distance measures. One distance measure can be suitable for one data set while some other distance measure can be suitable for some other data sets. So selecting the appropriate distance measure is also difficult. To address this issue, this paper presents an automatic framework which selects the suitable distance measure along with the appropriate partitioning. To encode the partitions, medoid based encoding is used. Several new mutation operations are used to explore the search space efficiently. Silhouette Index along with Xie-Beni Index are optimized simultaneously during the experiments for three bibliographic data sets. The results of our approach are compared with two existing well known techniques, DBLP and ArnetMiner. From the results it is clear that our proposed approach performs well.

Many of the real-life problems involve simultaneous optimization of multiple objectives. In recent years there is an enormous increase in the number of multi-objective optimization problems related to different real-life domains. Evolutionary algorithms are the most popular in solving these types of problems. The non-dominating sorting is one of the steps of any multiobjective evolutionary algorithms. This is used mostly to select the non-dominated set of solutions from a given population. In the past various efficient approaches are proposed in the literature to reduce the complexity of this step. As the evolutionary algorithms inhibit parallelism in it. But not all the existing non-dominating sorting approaches have the parallelism property. So in this paper, we have proposed a new approach named as DCNS (Divide and Conquer based Non-dominating Sorting) which inhibits parallelism in it. It has been shown theoretically and empirically that the proposed approach is computationally efficient than existing state-of-the-art methods.

In steady-state evolutionary algorithms, the parent population is updated each time once a new offspring solution is generated. Due to the updation of the parent population, the non-dominated sorting needs to be applied again and again. The repetition of non-dominated sorting makes steady-state algorithms computationally expensive. But the recent study has identified that the insertion of an offspring solution in the known non-domination level structure of solutions does not change the entire structure. So there is no need to apply the complete non-dominated sorting algorithm again and again. In this regard an efficient non-domination level update approach known as ENLU approach was proposed. In steady-state evolutionary algorithm, the same pair of solutions can be compared multiple times in different generations of the algorithm. In this paper, we have performed the same ENLU approach in a different way so that the same pair of solutions is compared only once if they are in the current population. The worst case time complexity of ENLU approach is O(MN2). So for G generations of the steady-state evolutionary algorithm, the worst case time complexity is GO(MN2). In this paper, we have utilized the same ENLU approach which performs the same number of comparisons all the times. The worst case time complexity of our approach is G(O(MN log N) + O(N2)). However, in terms of space complexity the proposed approach requires O(N2) space as compared to O(N) of the ENLU approach. So we have achieved the speedup at the cost of extra space. At the end, we have explored the possibility of parallelism to make the ENLU approach faster.

Traditionally the passwords are stored in hashed format. However, if the password file is compromised then by using the brute force attack there is a high chance that the original passwords can be leaked. False passwords -- also known as honeywords, are used to protect the original passwords from such leak. A good honeyword system is dependent on effective honeyword generation techniques. In this paper, the risk and limitations of some of the existing honeyword generation techniques have been identified as different notes. Three concepts -- modified tails, close number formation and caps key are introduced to address the existing issues. The experimental analysis shows that the proposed techniques with some preprocessing can protect high percentage of passwords. Finally a comparative analysis is presented to show how the proposed approaches stand with respect to the existing honeyword generation approaches.

In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.

An access control system is often viewed as a state transition system. Given a set of access control policies, a general safety requirement in such a system is to determine whether a desirable property is satisfied in all the reachable states. Such an analysis calls for formal verification. While formal analysis on traditional RBAC has been done to some extent, the extensions of RBAC lack such an analysis. In this paper, we propose a formal technique to perform security analysis on the Generalized Temporal RBAC (GTRBAC) model which can be used to express a wide range of temporal constraints on different RBAC components like role, user and permission. In the proposed approach, at first the GTRBAC system is mapped to a state transition system built using timed automata. Characteristics of each role, user and permission are captured with the help of timed automata. A single global clock is used to express the various temporal constraints supported in a GTRBAC model. Next, a set of safety and liveness properties is specified using computation tree logic (CTL). Model checking based formal verification is then done to verify the properties against the model to determine if the system is secure with respect to a given set of access control policies. Both time and space analysis has been done for studying the performance of the approach under different configurations.

Role based access control (RBAC) is an established paradigm in current enterprise resource protection environment. However, with the proliferation of mobile computing, it is being frequently observed that the RBAC access decision is directly influenced by the spatiotemporal context of both the subjects and the objects in the system. Currently, there exists few models which can handle spatiotemporal security policy on top of the classical RBAC. In this paper, an XML based policy specification framework is proposed for a spatiotemporal RBAC model. The framework is built on top of a spatiotemporal RBAC model known as ESTARBAC. It incorporates different constraints such as role hierarchy, separation of duty and cardinality, along with other constraints dependent on spatiotemporal conditions. The underlying model supports spatiotemporal role and permission extents. Use of such extents allows to specify a wide variety of spatiotemporal access control policies. The framework facilitates the administration task of a large organization by providing a convenient and efficient way of managing access control policies.

Role Based Access Control (RBAC) is arguably the most common access control mechanism today due to its applicability at various levels of authorization in a system. Time varying nature of access control in RBAC administered systems is often implemented through Temporal-RBAC - an extension of RBAC in the temporal domain. In this paper, we propose an initial approach towards verification of security properties of a Temporal-RBAC system. Each role is mapped to a timed automaton. A controller automaton is used to activate and deactivate various roles. Security properties are specified using Computation Tree Logic (CTL) and are verified with the help of a model checking tool named Uppaal. We have specifically considered reachability, safety and liveness properties to show the usefulness of our approach.