Menu
filter by: Publication Year
(Descending)
Publication Date: 2017
Telecommunication Systems (10184864) 64(2)pp. 367-390
Designing QoS-aware medium access control (MAC) scheme is a challenging issue in vehicular ad hoc networks. Proportional fairness and bandwidth utilization are among the significant requirements that should be taken into account by a MAC scheme. In this paper, a bandwidth-efficient and fair multichannel MAC protocol is proposed to address these two requirements, specifically in vehicle-to-vehicle communications. The proposed scheme is based on clustering of vehicles and exploits time division multiple access (TDMA) method alongside the carrier sense multiple access with collision avoidance mechanism to allocate DSRC-based resources in a different manner from IEEE 802.11p/IEEE 1609.4 protocols. It divides each channel into aligned dynamic-sized time frames. In each time frame, in a fully TDMA-based period, transmission opportunities are assigned to vehicles letting them have dedicated transmissions on the service and control channels. The maximum number of transmission opportunities per each frame is determined by the cluster head (CH) based on a defined optimization problem which aims at maximizing both proportional fairness and bandwidth utilization. Furthermore, the bandwidth utilization is assumed to be enhanced more through reallocation of unused transmission opportunities in each time frame, using a proposed reallocation algorithm. The proposed MAC protocol is treated as a lightweight scheme such that various types of unicast, multicast and broadcast communications are possible within the cluster without involving the CH. Evaluation results show that the proposed scheme has more than 90 % achievement in terms of proportional fairness and bandwidth utilization simultaneously, and in this case, has a considerable superiority over TC-MAC. In addition, using the proposed scheme, the satisfaction level of vehicles is preserved appropriately. © 2016, Springer Science+Business Media New York.
Publication Date: 2011
pp. 422-427
Combinatorial auctions are auctions in which bidders bid on combinations of items, bundles, instead of on individual items. In these auctions, bidders always tend to construct and bid on the most beneficial bundles of items, while facing a substantial number of items. Since there are a huge number of items available in a combinatorial auction, deciding on which items to put in bundles is a challenge for bidders. In combinatorial auctions, bundling of items and bidding on the best possible bundles are of great importance and developing an efficient bidding strategy can increase quality of the auctions considerably. In this paper, we have proposed an efficient bidding strategy. Performance of the proposed strategy in various markets has been simulated and compared with the bidding strategies already available in the literature. The obtained results show that in comparison with the previously available bidding strategies, the proposed strategy is more beneficial to both bidders and auctioneer, especially in markets where there is a considerable difference between values of items. © 2011 IEEE.
Auctions have been as a competitive method of buying and selling valuable or rare items for a long time. Single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. Double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. Nonetheless, this adds the complexity of the auction. Any auction mechanism needs to be budget balanced, Pareto optimal, individually rational, and coalition-proof. Satisfying all these properties is not so much trivial so that no multi-attribute double auction mechanism could address all these limitations. This research analyzes and compares the GM, timestamp-based and social-welfare maximization mechanisms for multi-attribute double auctions. The analysis of the simulation results shows that the algorithm proposed by Gimple and Makio satisfies more properties compared to other methods for such an auction mechanism. This multi-attribute double auction mechanism is based on game theory and behaves fairer in matching and arbitration. © 2013 IEEE.
Publication Date: 2025
Journal of Supercomputing (15730484) 81(14)
Steganography is a technique to hide the presence of secret communication and can be used when one of the communication elements is under the enemy’s influence. The primary measure to evaluate steganography methods in a specific capacity is security. Therefore, in a certain capacity, reducing the number of changes in the cover media leads to a higher embedding efficiency and, thus, higher security of a steganography method. Generally, security and capacity conflict and the increase of one lead to the decrease of the other. A single criterion representing security and capacity simultaneously can help compare steganography methods. Exploiting modification direction (EMD) and methods based on it are a type of steganography techniques that optimize the number of changes resulting from embedding (security). Despite their effectiveness, existing evaluation metrics for EMD-based methods lack precision and comprehensiveness. The present study aims to provide an evaluation criterion for this group of steganography methods. In this study, after a general review and comparison of EMD-based steganography techniques, a method is presented for their precise comparison from the perspective of embedding efficiency. Initially, we conduct a thorough review and comparative analysis of existing EMD-based steganography methods to identify their strengths and limitations. Building on this foundation, we introduce an enhanced embedding efficiency formula that accurately quantifies the impact of one or more-pixel changes, providing a more nuanced assessment of embedding performance compared to traditional metrics. Our results demonstrate that the proposed embedding efficiency formula offers superior performance evaluation, particularly in scenarios involving multiple pixel alterations. Furthermore, we establish an upper bound analysis to determine the theoretical maximum embedding efficiency achievable for any given capacity. This upper bound serves as a benchmark for assessing the optimal performance of EMD-based methods. Finally, leveraging the upper bound, we present an additional evaluation criterion that facilitates a more precise and meaningful comparison of EMD-based steganography methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2021
Peer-to-Peer Networking and Applications (19366450) 14(2)pp. 781-793
Internet of Things (IoT) is expected to empower all aspects of the Intelligent Transportation System (ITS), the main goal of which is to improve transportation safety. However, due to high demands by the increasing number of associated vehicles, the allocated bandwidth of ITS is inadequate. Cognitive Radio (CR) technology can be used as a solution for this high demand level. In CR, the pre-allocated spectrum bands are sensed to find the existing holes, caused by the absence of primary users. Cooperative spectrum sensing is an efficient tool for the detection of free spectrum bands that increase the probability of correct detection. In this paper, a distributed cooperative spectrum sensing technique is proposed using the consensus algorithm which is a distributed data aggregation mechanism whereby each vehicle combines the results received from its neighbors’ spectrum sensing. The combined results are repeatedly shared and combined such that all vehicles reach the same results. In vehicular networks, due to the vehicle’s movement, the number of its neighbors changes dynamically. Therefore, considering the vehicle’s mobility is essential in the spectrum sensing process. The consensus algorithm which is a data aggregation method is used to increase the probability of correct detection, and thus to reduce the number of collisions in the spectrum acquisition process. In our method, each vehicle accurately selects a number of its neighbors dynamically, and involves them in the decision-making process. Moreover, separate weights determined based on the entropy of their information are assigned to the sensing results of the selected neighbors. In this way, even if the vehicles are affected by fading or shadowing, they can make more accurate decisions using the sensing results received from other vehicles. The simulation results of the proposed method show that it increases the probability of correctly detecting free spectrum bands as well as convergence speed. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
Publication Date: 2011
Wireless Communications and Mobile Computing (discontinued) (15308677) 11(6)pp. 723-741
Growing demands for pervasive and ubiquitous services over wireless mobile networks and evolution of such networks towards heterogeneous solutions have emphasized the necessity of more intelligent handoff decisions. The existing handoff management methods in the literature are mostly using signal strength measurements and other link quality evaluations not addressing the knowledge about context of mobile devices, users and networks. Recently, context-aware handoff management has been considered as a novel candidate for fourth generation (4G) wireless technology. In this paper, user perceived quality of service has been considered in addition to traditional contexts such as user preferences, application requirements, network parameters and link quality for decision making. User perceived quality (UPQ) has been employed as a trigger source, in addition to link layer triggers which are emerged using media independent handover (MIH) event service. This paper presents a policy based mechanism for handoff decision making where fuzzy petri nets (FPNs) have been utilized as its evaluation algorithm. A case study has been provided by simulations to show the usability and user level satisfaction. Simulation results show superior performance in terms of UPQ, jitter and packet delivery measures. Copyright © 2009 John Wiley & Sons, Ltd.
Publication Date: 2018
Telecommunication Systems (10184864) 69(4)pp. 415-429
Vehicular Ad hoc Network (VANET) enables high speed vehicles to communicate with each other. This kind of communication can provide road safety and passengers’ comfort. Covert channels are used to transmit information secretly over the network. Network covert channel is not only used as a hacking tool, but also used to convey secret information such as private keys. Unlike wired and conventional wireless networks, few studies are conducted on covert communication in VANET. The goal of this paper is to develop a hybrid (timing and storage) covert channel in VANET. In the timing part, covert messages are sent by altering the timing pattern of the service and control packets. The proposed covert timing algorithm is dynamically changed based on the vehicular traffic volume in the transmitter’s radio range. This dynamism is used to achieve better covert capacity with an acceptable error rate. On the other hand, some fields of the periodic status messages, sent in the control channel, are utilized in the storage part. An encoding algorithm is also proposed to embed the covert data in the mentioned covert timing and storage opportunities. The encoding algorithm provides a high embedding capacity, even if the number of opportunities’ possible values is not any power of two. Finally, the transmitted secret data volume, the packet loss ratio, the channel error rate and the effect of the proposed method on other vehicles’ throughput are evaluated in a simulation process. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2009
International Journal of Human Computer Studies (10959300) 67(1)pp. 1-35
Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains. © 2008 Elsevier Ltd. All rights reserved.
Publication Date: 2024
pp. 18-23
Reducing the size of transistors in multi-core processors has caused reduced energy consumption, fixed power density, and exponential growth in performance. In new generations of integrated circuits, despite the smaller transistors, the energy consumption has not been scaled down anymore; therefore, with constant power consumption in each transistor, the increasing number of transistors leads to an exponential growth in total power consumption, thermal concerns and dark silicon problems. In addition, the effects of aging are essential for the design and construction of integrated circuits. Since aging reduces the service lifetime of a circuit, this deterioration can affect all aspects including performance and reliability. Dynamic voltage and frequency scaling are power management methods used to control the workload. This paper presents a supervised learning-based method that predicts the performance of the system through some available input features and then adjusts the appropriate frequency and voltage for each workload. Simulation results show 95% accuracy in a multi-core processor using the decision tree method. © 2024 IEEE.
Publication Date: 2017
pp. 42-47
D2D communications empower operators to offer their services at the highest level of quality provided that issues concerning availability and security are addressed first. The explosive amount of mobile data traffic, on one hand, and the growing demand for available services on the other hand, motivate us to propose a secure, lightweight and available data sharing scheme for D2D communications. Data sharing, an increasingly popular service among mobile users, could play a noticeable role in offloading the traffic data from operators if handled by D2D communications. In this paper, we propose an efficient protocol for secure data sharing in D2D communication. In the proposed protocol, the major security challenges about data sharing like, data confidentiality, integrity, detecting message modification, and preventing the propagation of malformed data are considered. Additionally, not only unauthorized users are banned from using our service, but also by keeping records about the history of the authorized users actions, we are able to punish misbehaving users, if their malicious behavior exceeds a threshold. The evaluation of the proposed protocol proves that it is more lightweight than the previous works and supports the security requirements of data sharing scheme. © 2017 IEEE.
Publication Date: 2023
ISeCure (20083076) 15(3 Special Issue)pp. 117-128
Today, passive RFID tags have many applications in various fields such as healthcare, transportation, asset management, and supply chain management. In some of these applications, a group of tags need to prove they are present in the same place at the same time. To solve this problem, many protocols have been proposed so far, and each of them has been able to solve some security and performance problems, but unfortunately, many of these protocols have security vulnerabilities or do not have the necessary performance to run on passive RFID tags. In this study, a secure and lightweight protocol for RFID tags grouping proof called LSGPP is proposed. In this protocol, the reader is an untrusted entity, in other words, the protocol is secure even if the reader is hijacked by an attacker. This study shows that the LSGPP protocol is secure against tracking, eavesdropping, replay, concurrency, impersonation, desynchronization, denial of service (DoS), proof forgery, message integrity, man-in-the-middle, secret disclosure, denial of proof (DoP), and unlinkability attacks, and supports anonymity and forward secrecy features. Also, in this study, the notion of RFID reader compromised attack is introduced, and it is shown that, unlike its predecessors, the LSGPP protocol is also secure against this attack. Also, using the Proverif tool, it is shown that the proposed protocol provides confidentiality and authentication features. The LSGPP protocol uses lightweight operations affordable for passive RFID tags and is shown to be compliant with the EPC C1G2 standard. © 2023 ISC. All rights reserved.
Publication Date: 2017
International Journal of Communication Systems (10991131) 30(3)
The widespread use of Session Initiation Protocol as a signalling protocol has created various challenges. An important one is that its throughput can be severely degraded when an overload happens in the proxy server because of several retransmissions from the user agent. One common approach to overcome this problem is ‘load balancing’. A balancer needs to know the status of proxy servers, which are continuously gathered implicitly or explicitly. Implicit methods have averagely less overhead than explicit ones. This paper attempts to prevent throughput reduction by balancing the loads among available proxy servers properly using an implicit mechanism called History Weighted Average Response time. The proposed algorithm is robust because it incurs no extra processing to proxy servers. The novelty of the mechanism is making use of ‘response time history’ to estimate the load being currently processed on servers. By implementing in a real testbed, throughput and scalability are improved compared with an important state-of-the-art similar algorithm. This improvement stems from no need for modification in SIP protocol, easy implementation and application, simple computations for making decision and no need for extra feedback between servers and load balancer. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
Publication Date: 2016
IEEE Transactions on Network and Service Management (19324537) 13(4)pp. 806-822
Network functions virtualization provides opportunities to design, deploy, and manage networking services. It utilizes cloud computing virtualization services that run on high-volume servers, switches, and storage hardware to virtualize network functions. Virtualization techniques can be used in IP multimedia subsystem (IMS) cloud computing to develop different networking functions (e.g., load balancing and call admission control). IMS network signaling happens through session initiation protocol (SIP). An open issue is the control of overload that occurs when an SIP server lacks sufficient CPU and memory resources to process all messages. This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-hosted SIP servers. VLB-CAC determines the optimal "call admission rates" and "signaling paths" for admitted calls along with the optimal allocation of CPU and memory resources of the SIP servers. This optimal solution is derived through a new linear programming model. This model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped with an autoscaler to overcome resource limitations. The proposed scheme is implemented in smart applications on virtual infrastructure (SAVI) which serves as a virtual testbed. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed work. © 2016 IEEE.
Khademali, M. ,
Aghamohammadi, F. ,
Kaedi, M. ,
Nasiri, A. Publication Date: 2024
pp. 167-171
A recommender system typically assumes that each row of the user-item rating matrix reflects the preferences of a single user. However, in many cases, an account is shared among multiple household members, resulting in mixed ratings data that do not accurately represent individual preferences. As a consequence, the recommendations will fail to align with the specific interests of each user. To address this issue, we introduce the concept of a "user character," which represents a common latent factor in both movie and account features. By establishing a movie feature matrix based on these character representations, we can identify the presence of different characters in shared accounts. This is achieved by factoring the account feature binary matrix from the rating matrix, a process that can be modeled as a binary quadratic optimization problem. For scalability, we relax the binary constraint using a penalty function and approximate the solutions through the gradient descent method. Additionally, we apply a thresholding function to obtain binary solutions that reveal the user characters within each account. Once we identify the characters associated with each account, we can learn users' distinct preferences through a demixing procedure. This allows us to reconstruct the rating matrix so that each row accurately represents a single user's preferences. To evaluate our method, we generated a shared account dataset from MovieLens ratings based on the CAMRa2011 dataset. Experiments conducted on this dataset demonstrate the effectiveness of our proposed approach. © 2024 IEEE.
Publication Date: 2018
International Journal of Applied Metaheuristic Computing (19478283) 9(1)pp. 40-48
This paper develops a nature-inspired metaheuristic algorithm named sun and leaf optimization (SLO) which is inspired by the effect of sunlight on the leaves germination. In SLO, candidate solutions in the state space are considered as leaves grown on a tree, and high-quality solutions are considered as greener leaves germinated in the direction of sunlight. On a tree, usually greener leaves are found closed to each other, because such area is probably exposed more to the sun and hence it is suitable for hosting other greener leaves. Inspired by this phenomenon, in SLO, during the search, the authors take the existence of high quality solutions as a sign of promising areas for finding optimum; thus, they generate more candidate solutions near the higher quality solutions to search those areas more painstakingly. Wind effect is imitated to escape the local optima. The evaluation results demonstrate the high performance of proposed algorithm. Copyright © 2018, IGI Global.
Publication Date: 2024
BMC Medical Informatics and Decision Making (14726947) 24(1)
Background: DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach. Methods: In the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach. Results: By applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced. Conclusions: The combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis. Clinical trial number: Not applicable. © The Author(s) 2024.
Salehnia, T. ,
Miarnaeimi, F. ,
Izadi, S. ,
Ahmadi, M. ,
Montazerolghaem, A. ,
Mirjalili, S. ,
Abualigah, L. Publication Date: 2023
pp. 625-651
Finding the threshold vector that gives the best performance of the image segmentation system is significant in Multi-level Thresholding Image Segmentation (MTIS) methods. Meta-Heuristic (MH) algorithms are among the techniques that can find reasonably good optimal thresholds and require reasonable computational resources. We use the combination model of the Whale Optimization Algorithm (WOA) and in conjunction with Moth-Flame Optimization (MFO) for MTIS. In MFWOA, the solutions during the exploitation phase are updated using the operators of WOA, and in the exploration phase, only the operators of MFO are used. The Inverse Otsu (IO) Function is used as Fitness Function for MFWOA. Experiments in image segmentation show that the proposed MFOWOA method is better than the compared algorithms in terms of accuracy as indicated by two performance measures: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). It is also observed that the MFWOA algorithm is faster than WOA and slower than MFO in terms of execution time evaluation metric. In some cases, the proposed algorithm is faster than other algorithms. The results show demonstrate that the hybrid MFWOA algorithm solves MTIS problems better than both WOA and MFO algorithms and can obtain better thresholds that increase the performance of the MTIS system. © 2024 Elsevier Inc. All rights reserved.
Publication Date: 2014
Communications in Computer and Information Science (18650937) 428pp. 145-154
Due to resource scarcity of mobile devices in Mobile Cloud Computing (MCC), intensive computing applications are offloaded into the cloud. There is a three-tier architecture for MCC consisting of distant cloud servers, nearby cloudlets and adjacent mobile devices. In this paper, we consider third tier. We propose an Optimal Fair Multi-criteria Resource Allocation (OFMRA) algorithm that minimizes the completion time of offloading applications along maximizing lifetime of mobile devices. Furthermore to stimulate selfish devices to participate in offloading, a virtual price based incentive mechanism is presented. The paper also designs an Offloading Mobile Cloud Framework (OMCF) which collects profile information and handles the offloading process. A prototype of the proposed method has been implemented and evaluated using a high computational load application. The results show that the proposed algorithm manages the tradeoff between optimizing completion time and energy well and improves the performance of offloading using the incentive mechanism. © Springer International Publishing Switzerland 2014.
Publication Date: 2016
pp. 117-122
Considering the development of mobile payment systems and feasibility and suitability of payment protocols we need to provide security requirements of users as well. In this paper we first introduce LMPP and MPCP protocols and show how these two protocols are unable to satisfy anonymity and unlinkability of merchant to issuer. Then we propose a lightweight mobile payment protocol that is based on LMPP protocol in which the mobile network operators are involved instead of financial institutions. To have a better performance in mobile networks, we employ symmetric key primitives in the proposed protocol. Moreover, our protocol provides anonymity and unlinkability of merchant to issuer as well as other main security requirements. © 2016 IEEE.
Sajadieh, M. ,
Mirzaei, A. ,
Mala, H. ,
Rijmen, V. Publication Date: 2017
Designs, Codes, and Cryptography (09251022) 83(2)pp. 327-343
Security against differential and linear cryptanalysis is an essential requirement for modern block ciphers. This measure is usually evaluated by finding a lower bound for the minimum number of active S-boxes. The 128-bit block cipher AES which was adopted by National Institute of Standards and Technology (NIST) as a symmetric encryption standard in 2001 is a member of Rijndael family of block ciphers. For Rijndael, the block length and the key length can be independently specified to 128, 192 or 256 bits. It has been proved that for all variants of Rijndael the lower bound of the number of active S-boxes for any 4-round differential or linear trail is 25, and for 4r (r≥ 1) rounds 25r active S-boxes is a tight bound only for Rijndael with block length 128. In this paper, a new counting method is introduced to find tighter lower bounds for the minimum number of active S-boxes for several consecutive rounds of Rijndael with larger block lengths. The new method shows that 12 and 14 rounds of Rijndael with 192-bit block length have at least 87 and 103 active S-boxes, respectively. Also the corresponding bounds for Rijndael with 256-bit block are 105 and 120, respectively. Additionally, a modified version of Rijndael-192 is proposed for which the minimum number of active S-boxes is more than that of Rijndael-192. Moreover, we extend the method to obtain a better lower bound for the number of active S-boxes for the block cipher 3D. Our counting method shows that, for example, 20 and 22 rounds of 3D have at least 185 and 205 active S-boxes, respectively. © 2016, Springer Science+Business Media New York.
Publication Date: 2017
Journal Of Medical Signals And Sensors (22287477) 7(3)pp. 163-169
Wireless body area networks consist of several devices placed on the human body, sensing vital signs and providing remote recognition of health disorders. Low power consumption is crucial in these networks. A new energy-efficient topology is provided in this paper, considering relay and sensor nodes' energy consumption and network maintenance costs. In this topology design, relay nodes, placed on the cloth, are used to help the sensor nodes forwarding data to the sink. Relay nodes' situation is determined such that the relay nodes' energy consumption merges the uniform distribution. Simulation results show that the proposed method increases the lifetime of the network with nearly uniform distribution of the relay nodes' energy consumption. Furthermore, this technique simultaneously reduces network maintenance costs and continuous replacements of the designer clothing. The proposed method also determines the way by which the network traffic is split and multipath routed to the sink.
Publication Date: 2017
2018pp. 165-171
The employee's performance evaluation in organizations is one of the major challenges of the management that has been received a great attention by researchers and managers. The main problem of the current performance evaluation methods is the impact of individual emotions and employee judgments on the evaluation process, which reduces results in the biased evaluation. To solve this problem, in this paper a new method based on Fuzzy AHP and fuzzy TOPSIS for employee performance evaluation are presented. First, the weight of the evaluation criteria is calculated using the Fuzzy AHP method. Next, each employee's performance is scored by weighted criteria using Fuzzy TOPSIS method. The proposed method has been tested on employees of the Entekhab Industrial Group. The results indicate that the proposed method is more effective than other evaluation methods in employee performance evaluation. © 2017 IEEE.
Mala, H. ,
Dakhil-alian, M. ,
Brenjkoub, M. Publication Date: 2006
2pp. 3304-3308
Proxy signature schemes allow a proxy signer to generate a proxy signature on behalf of an original signer. In this paper we propose an Identity-based proxy signature scheme from bilinear pairings. In comparison with the Xu et al's scheme, our scheme is more efficient in computation and requires fewer pairing operations especially in verification phase. © 2006 IEEE.
Publication Date: 2016
pp. 52-59
Internet of Things (IoT) is a network of objects which enables them to collect vital information. As a result, privacy and anonymity in IoT are the most important issues. So far, many protocols have been proposed to provide authentication mechanism in IoT networks. Recently, Amin et al proposed a three-factor authenticated protocol for IoT networks that is claimed to be secure. In this paper, we challenge this claim and show that this protocol is vulnerable against the replay attack and DoS attack. Moreover, inspired by this protocol, we propose a secure authenticated key exchange protocol with the same assumptions. Our analysis shows that our proposed protocol is more efficient than Amin et al protocol. © 2016 IEEE.
Publication Date: 2015
The Arabian Journal For Science And Engineering (2193567X) 40(2)pp. 487-499
Delay and capacity are two important parameters in mobile ad hoc networks (MANETs). Increasing the network capacity almost leads to delay increases, as well. Many recent works have been conducted to achieve both desirable capacity and delay, simultaneously. To achieve such aim, this study proposes a new reactive routing algorithm. This algorithm modifies multi-hop Dynamic Virtual Router algorithm to overcome the performance limits of MANETs. Mobility metrics are defined to estimate the mobility degree of the nodes’ neighborhood. A new route setup process is defined; using the estimated information and a local repair mechanism is also introduced in the new proposed algorithm. In this local repair mechanism, a new route is sought between the repairing node and its next hop on the communication path. Simulation study shows that the proposed algorithm significantly improves the network performance, including throughput and delay; so that, the increasing overhead is not remarkable considering the great performance improvement of the algorithm. © 2014, King Fahd University of Petroleum and Minerals.
Publication Date: 2018
pp. 120-125
Network traffic classification is an essential requirement for network management. Various approaches have been developed for network traffic classification. Traditional approaches such as analysis of port number or payload have some limitations. For example, using port numbers for traffic classification fails if an application uses dynamic port number or applies encryption methods. To address such limitations, modern traffic classification methods employ machine learning techniques. However, machine learning-based traffic classification needs a large labeled data to extract accurate classification model which is expensive and time-consuming. To overcome this issue, we propose a new semi-supervised method for traffic classification based on x-means clustering algorithm and a new label propagation technique. The accuracy of the proposed method tested on Moore's dataset is 0.95 that shows its effectiveness for learning a network traffic classifier using a limited labeled data. © 2018 IEEE.
Publication Date: 2014
pp. 509-514
Widespread use of SIP as a signalling protocol in VoIP networks is the main reason for tackling various challenges. SIP throughput can severely be degraded when an overload situation happens in the proxy servers due to several retransmissions from user agents. In this paper we try to prevent throughput reduction by properly distributing the loads over available proxy servers. The proposed scheme utilizes response time of the servers as the main decision factor. The algorithm is implemented in a real environment using Spirent and Asterisk servers as call generator and load balancer respectively. The results of comparing the proposed method with some well-known algorithms indicate considerable throughput improvement up to 15% with a Round-Robin algorithm. © 2014 IEEE.
Publication Date: 2013
Educational Technology And Society (11763647) 16(3)pp. 88-101
Assessment is one of the most essential parts of any instructive learning process which aims to evaluate a learner's knowledge about learning concepts. In this work, a new method for learner assessment based on learner annotations is presented. The proposed method exploits the M-BLEU algorithm to find the most similar reference annotations and then the learner annotation will be processed further to check essential words, words order and contradictions. To examine this new approach, a virtual learning environment was designed and implemented in which assessment of the learner's knowledge is performed on the basis of main and sub concepts. These concepts are delivered by means of course contents and the learning environment guides the user to annotate concepts. Evaluation results show that our designed system can effectively assess learner's knowledge. The benefit of suggested assessment method is its implicitness of assessment approach. Furthermore the correct annotations can be used to help the users remembering concepts by reviewing their annotations. © International Forum of Educational Technology & Society (IFETS).
Publication Date: 2017
Pattern Analysis and Applications (1433755X) 20(3)pp. 701-715
Supervised clustering is a new research area that aims to improve unsupervised clustering algorithms exploiting supervised information. Today, there are several clustering algorithms, but the effective supervised cluster adjustment method which is able to adjust the resulting clusters, regardless of applied clustering algorithm has not been presented yet. In this paper, we propose a new supervised cluster adjustment method which can be applied to any clustering algorithm. Since the adjustment method is based on finding the nearest neighbors, a novel exact nearest neighbor search algorithm is also introduced which is significantly faster than the classic one. Several datasets and clustering evaluation metrics are employed to examine the effectiveness of the proposed cluster adjustment method and the proposed fast exact nearest neighbor algorithm comprehensively. The experimental results show that the proposed algorithms are significantly effective in improving clusters and accelerating nearest neighbor searches. © 2015, Springer-Verlag London.
Publication Date: 2008
Simulation and Gaming (1552826X) 39(1)pp. 83-100
Distributed Artificial Intelligence techniques have evolved toward multi-agent systems (MASs) where agents solve specific problems. Bargaining is a challenging area well-explored in both MAS and economics. To make agents more human-like and to increase their flexibility to reach an agreement, the authors investigated the role of personality behaviors of participants in a multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. The authors simulate a computational approach based on a heuristic bargaining protocol and personality model on artificial stereotypes. The results suggest compound behaviors appropriate to gain the best overall utility in the role of buyer and seller and with regard to social welfare and market activeness. This generic personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in areas involving negotiation and bargaining. © 2008 Sage Publications.
1 2 3 ... 12
Address Isfahan, Azadi Square, University of Isfahan
