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1 |
A jamming attack detection technique for opportunistic networks
یک تکنیک تشخیص حمله پارازیت برای شبکه های فرصت طلب-2022 Opportunistic networks (OppNets) are dispersed in nature, with nodes acting as resource
restrictions, with intermittent connectivity. These nodes are subject to various types of attacks,
posing a security risk in data transmission. One of the most common attacks that cause jamming
among the message forwarding nodes in infrastructure-less networks is Denial of Service (DoS)
attack. Most of the methods addressing this type of attack rely on cryptographic algorithms,
which are too difficult to implement. In this paper, a novel jamming attack detection technique
(JADT) for OppNets, is proposed, which relies on the use of some statistical measures collected
from the relay nodes and a prescribed threshold on the packet delivery ratio (PDR) to discover
a jamming attack while decrypting the acknowledgement, stopping the message transmission
and rebroadcasting the message through a different channel. The proposed JADT is evaluated
using the ONE simulator, showing its superiority against the Fuzzy Geocasting mechanism in
Opportunistic Networks (F-GSAF) scheme in terms of packet delivery ratio and overhead ratio,
under varying TTL and buffer size.
Keywords: Jamming detection | Opportunistic networks | Routing | Statistical information | Energy |
مقاله انگلیسی |
2 |
An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network
یک مسیریابی فرصت طلبانه جغرافیایی معتبر مبتنی بر بیومتریک برای برنامه های IoT با استفاده از شبکه حسگر بی سیم امن-2021 The applications of Wireless Sensor Networks (WSNs) are been broadly utilized in the field of Internet of Things (IoT) under communication framework. Notwithstanding services gave by the WSNs, numerous IoT-related applications necessitate reliable and secure delivery of data over unsteady remote connec tions. In-order to ensure secure and reliable delivery of data, many existing paper works accomplish authentication based routing algorithms with numerous forwarders within the Wireless Sensor Networks. Be that as it may; these types of approaches are vulnerable to genuine attacks like Denial of Service (DoS), where countless duplicate data packets are intentionally dispatched to destination node which disturbs the typical activities of wireless sensor networks. So, here we propose a new scheme of security algorithm for the wireless sensor networks. Our method, Biometric based-Authenticated Geographic Opportunistic Routing (BAGOR) algorithm depends on the user biometrics to shield the violation of DoS attacks, in order to meet out the validness requirements and reliability in the network. By examining biometric and statistic state information (SSI) of remote connections, BAGOR uses a trust model as statistic state information to get better proficiency of packet delivery. Dissimilar to past pioneering routing algorithm, BAGOR guarantees data honesty by building up an entropy-deployed selective validation algorithm and can detach DoS aggressors and reduce the computational expense. Thus, the eveloped procedure is assessed and compared with already existing security techniques. The simulations show that BAGOR decreasing system traffic, shielding against Denial of Service attacks, and expanding the lifetime of a sensor node in the network. Thus, the usefulness and execution of the whole system is enhanced.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering. Keywords: Biometric authentication | BAGOR algorithm | Denial-of-Service attacks | Geographic opportunistic routing | Statistic state information |
مقاله انگلیسی |
3 |
Community-based “Piggy-back Network” utilizing Local Fixed & Mobile Resources supported by Heterogeneous Wireless & AI-based Mobility Prediction
"شبکه Piggy-back" مستقر در جامعه با استفاده از منابع محلی ثابت و موبایل پشتیبانی شده توسط بی سیم ناهمگن و پیش بینی تحرک مبتنی بر هوش مصنوعی-2020 This paper proposes a concept to construct a
community-based cross-industrial data/contents delivery platform
named “Piggy-back network,” which utilizes alreadyexisting
local fixed and mobile resources in a smart city. These
fixed and mobile resources are assumed to equip store-carryforwarding-
based (SCF-based) wireless data sharing functions,
i.e., a short-range but extremely high-speed millimeter-wave
device, a mid/long-range but low-speed microwave device, and
data storage buffer. It is discussed that the data delivery
performance of such SCF-based platform could exceed the one
when using wired/wireless infrastructure directly, and it will
be significantly improved if an AI-based mobility prediction
engine recommends the human drivers or the driving controllers
of future automated driving vehicles to detour and/or stop by
some specific locations. It is theoretically shown that the mobile
resources can potentially deliver high-volume data with shorter
time than using wired/wireless network infrastructures under
some conditions. The real commercial mobilities’ trajectory data
obtained experimentally in the city of Kakogawa, Japan, are
analyzed and the potential of the data delivery performance is
estimated. Index Terms: Piggy-back network | community-based cross-industrial data/contents delivery platform | Store-Carry- Forwarding | Opportunistic Network | AI-based mobility prediction |
مقاله انگلیسی |
4 |
Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020 With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular
and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity
communication is regarded as a potential solution. It can use unlicensed bands to forward content to users
under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is
easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the
location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition,
deep learning-based methods almost need a lot of training time. Based on real-time requirements of the
network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing
(MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents
offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation. Keywords: Opportunistic networks | MEC | Offloading | Content caching |
مقاله انگلیسی |
5 |
Deep reinforcement learning based preventive maintenance policy for serial production lines
یادگیری تقویتی عمیق مبتنی بر سیاست یشگیری برای خطوط تولید متوالی-2020 In the manufacturing industry, the preventive maintenance (PM) is a common practice to reduce random
machine failures by replacing/repairing the aged machines or parts. The decision on when and where the
preventive maintenance needs to be carried out is nontrivial due to the complex and stochastic nature of
a serial production line with intermediate buffers. In order to improve the cost efficiency of the serial production
lines, a deep reinforcement learning based approach is proposed to obtain PM policy. A novel
modeling method for the serial production line is adopted during the learning process. A reward function
is proposed based on the system production loss evaluation. The algorithm based on the Double Deep QNetwork
is applied to learn the PM policy. Using the simulation study, the learning algorithm is proved
effective in delivering PM policy that leads to an increased throughput and reduced cost. Interestingly,
the learned policy is found to frequently conduct ‘‘group maintenance” and ‘‘opportunistic maintenance”,
although their concepts and rules are not provided during the learning process. This finding further
demonstrates that the problem formulation, the proposed algorithm and the reward function setting
in this paper are effective. Keywords: Preventive maintenance | Production loss | Deep reinforcement learning | Serial production line | Group maintenance | Opportunistic maintenance |
مقاله انگلیسی |
6 |
Civil unrest, emergency powers, and spillover effects: A mixed methods analysis of the 2005 French riots
ناآرامی های غیرنظامی ، قدرت اضطراری و گسترش تأثیرات : تجزیه و تحلیل روش های مختلط از شورش های فرانسه در 2005-2020 From early to mid-November 2005, many French urban suburbs experienced riots. In the affected areas the government declared a state of emergency which gave the police ex- trajudicial powers. It remained in place until January. I investigate whether the riots gen- erated criminal spillovers, whether the emergency powers deterred criminal activity, and whether the police used those powers opportunistically to bust crime unrelated to the riots. I supplement linear regressions with a non-parametric bounded-variation assump- tions framework combined with a synthetic control approach, and interviews I conducted with two of the events’ key actors. Criminals did not take advantage of the riots to com- mit more crimes requiring planning. However, the riots triggered a surge of violent thefts. The state of emergency did not result in a decrease in delinquency. Several clues sug- gest a strategy of appeasement. Meanwhile, some serious crimes increased immediately after the riots ended, suggesting an emboldening effect. Evidence of police opportunism is scant. Keywords: Riots | Opportunism | Crime | Police | Emergency powers | Bounded variation assumptions |
مقاله انگلیسی |
7 |
A breakthrough in urban rain-harvesting schemes through planning for urban greening: Case studies from Stockholm and Barcelona
دستیابی به موفقیت در طرح های بارش باران شهری از طریق برنامه ریزی برای فضای سبز شهری: مطالعات موردی از استکهلم و بارسلونا-2020 A plethora of socioenvironmental issues, and growing concerns over the effects of climate change, are forcing
cities to rethink conventional urban water management practices. However, change towards more sustainable
practices has been remarkably slow. This paper examines two cases of greening projects aimed at urban rehabilitation
in Stockholm and Barcelona, which have turned into examples of innovative approaches to urban
rain management. Both cities share high densities and flooding problems in certain neighborhoods. Specifically,
the paper attempts to answer three questions: 1) what were the driving forces and key factors that facilitated the
breakthrough of urban rainwater-harvesting (URH) schemes based on the two cases?; 2) who were the actors
involved and what were their roles in moving towards URH schemes?; and 3) how can URH schemes become
part of multifunctional, sustainable urban systems? To answer these questions, the paper uses concepts of
adaptive context and capacity, and of actor agency, drawn from the transitions literature, and opportunistic and
guided flexibility planning drawn from the planning literature. Empirical material for both case studies was
obtained from policy documents and semi-structured interviews with key actors. The main results show first
political support for flexibility in public planning, the adaptive context and the capacity of the actors, especially
in taking advantage of windows of opportunity for the materialisation of new ideas. Second, the design and
implementation of these systems widened the number and scope of actors in urban water management, incorporating
new professionals such as architects and involving more city agencies and organizations. Third,
small scale URH systems contributed not only to control urban drainage but performed other functions such as
the much-needed greening of dense areas in both cities. Keywords: Barcelona | Opportunistic planning | Stockholm | Transition theory | Urban rainwater-harvesting systems | Urban rehabilitation |
مقاله انگلیسی |
8 |
The impact of abusing return policies: A newsvendor model with opportunistic consumers
تاثیر سوء استفاده از سیاست های بازگشتی: یک مدل روزنامه فروشی با مشتری های فرصت طلب-2018 Consumers may return a product for a variety of reasons, such as the product having the wrong color or size, having poor functionality, being damaged during shipment, or simply prompting regret for an impulsive purchase. Retailers generally provide lenient return policies not only because they may signal high quality but also because they act as risk relievers for consumers’ purchasing decision processes. However, increasing product returns have become particularly challenging for the efficient management of inventory. As such, at the crux of a holistic inventory model lies the understanding of consumer return behavior. In this study, we introduce a variant of the classical single-period inventory (newsvendor) model with returns, in which heterogeneous consumers decide, based on their post-purchase valuation of the product, whether to return the product after using it. From the perspective of the retailer, such deliberate returns may abuse the return policy, which in turn may exacerbate reverse logistics and environmental costs. To that end, we incorporate demand uncertainty and consumer valuation uncertainty by explicitly gauging return probabilities and differentiated salvage values into a newsvendor model. We derive analytical results for the profit-maximizing order quantity for a single-period product that comes with a retailer return policy and exclusively identify the impact of return type as abused or normal. Also offered are closed-form optimal solutions in the cases where market demand is exponentially or uniformly distributed. Structural and numerical results lend managerial insight into how optimal ordering amount, profit, return rates and salvage values change with the price, return window, and hassle cost of returning the product.
keywords: Inventory management |Consumer behavior |Product returns |Fraudulent proclivity |Uncertain demand |Return policy |
مقاله انگلیسی |
9 |
Environmental uncertainty, specific assets, and opportunism in 3PL relationships: A transaction cost economics perspective
عدم قطعیت محیطی، دارایی های ویژه و فرصت طلبی در روابط 3PL: یک دیدگاه اقتصادی هزینه ای تراکنشی-2018 Service provider opportunism is a serious concern in third party logistics (3PL) relationships. However, our knowledge about the antecedents of 3PL providers opportunism is limited. According to transaction cost economics (TCE), increased transaction costs cause opportunism. This study incorporates key TCE constructs (environmental uncertainty, specific assets, and opportunism) and conducts a transaction cost analysis. We argue that environmental uncertainty and specific assets create exchange hazards that result in opportunism. Meanwhile, specific assets reduce coordination costs raised by environmental uncertainty. Building on these arguments, this study tests a model that hypothesizes that environmental uncertainty (demand, supply, and technology uncertainty), and specific assets (user- and provider-specific assets) are positively related to opportunism, and that environmental uncertainty is positively related to specific assets. Structural equation modeling is used to examine data from 247 3PL relationships in China. The results show that demand uncertainty decreases opportunism, supply uncertainty increases opportunism, and technology uncertainty does not have a significant effect. User-specific assets increase opportunism, while provider-specific assets decrease opportunism. Demand and supply uncertainty have positive effects on user-specific assets, but non-significant effects on provider-specific assets, while technology uncertainty does not have a significant impact on user or provider-specific assets. In general, our findings are supported by the rationale of TCE, and industrial or cultural factors can explain several surprising findings. This study contributes to 3PL literature and practice.
keywords: Environmental uncertainty |Specific assets |Opportunism |3PL |Transaction cost economics |
مقاله انگلیسی |
10 |
Opportunistic mining of top-n high utility patterns
معدن فرصت طلب از بالا-N الگوهای مفید بالا-2018 Mining high utility patterns is an important data mining problem that is formulated as
finding patterns whose utilities are no less than a threshold. As the mining results are
very sensitive to such a threshold, it is difficult for users to specify an appropriate one.
An alternative formulation of the problem is to find the top-n high utility patterns. How
ever, the second formulation is more challenging because the corresponding threshold is
unknown in advance and the solution search space becomes even larger. When there are
very long patterns prior algorithms simply cannot work to mine top-n high utility patterns
even for very small n.
This paper proposes a novel algorithm for mining top-n high utility patterns that are
long. The proposed algorithm adopts an opportunistic pattern growth approach and pro
poses five opportunistic strategies for scalably maintaining shortlisted patterns, for effi
ciently computing utilities, and for estimating tight upper bounds to prune search space.
Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude
more efficient than the state-of-the-art top-n high utility pattern mining algorithms, and
it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms
that are tuned with an optimal threshold.
Keywords: Utility mining ، Pattern mining ، High utility patterns ، Frequent patterns ، Top-n interesting patterns |
مقاله انگلیسی |