Setup Phase Research Articles

Adaptive and Dynamic Mechanism for Round Length Determination in Cluster Based Wireless Sensor Networks

The resource-constrained nature of WSNs require efficient use of resources, especially energy, to prolong their lifetime. Clustering is one of the popular approaches to allocate the resources efficiently among the WSN nodes. In this work, we analyze the problem of round length determination in cluster based WSN which has severe impact on the energy efficiency. This problem is very important since round length determines how often the cluster head (CH) rotates or re-clustering process occurs. A longer round length will cause the CH nodes to operate for a long time and drain their energies faster than other nodes resulting in uneven energy consumption in the network, while a shorter round length results in considerable wastage of energy due to frequent running of the setup phase. Hence, we propose an adaptive and dynamic mechanism for round length determination in cluster based WSNs by adapting Behavior Curve Function modeled by quadratic Bezier curves, where we associate the remaining energy level of the cluster to its round operation length and to its assigned criticality which is defined based on network energy level. This helps to determine the number of frames in a round or how many times the data collection occurs in a cluster in a round and the criticality of the energy in the WSN. Simulation results reveal that the proposed mechanism has effectively reduced the energy consumption and improved the WSN lifetime in both homogeneous and heterogeneous network settings.

αSetup-AMG: an adaptive-setup-based parallel AMG solver for sequence of sparse linear systems

The algebraic multigrain (AMG) is one of the most frequently used algorithms for the solution of large-scale sparse linear systems in many realistic simulations of science and engineering applications. However, as the concurrency of supercomputers increasing, the AMG solver increasingly leads to poor parallel scalability due to its coarse-level construction in the setup phase. In this paper, to improve the parallel scalability of the traditional AMG to solve the sequence of sparse linear systems arising from PDE-based simulations, we propose a new AMG procedure called αSetup-AMG based on an adaptive setup strategy. The main idea behind αSetup-AMG is the introduction of a setup condition in the coarsening process so that the setup is constructed as it needed instead of constructing in advance via an independent phase in the traditional procedure. As a result, αSetup-AMG requires fewer setup cost and level numbers for the sequence of linear systems. The numerical results on thousands of cores for a radiation hydrodynamics simulation in the inertial confinement fusion (ICF) application show the significant improvement in the efficiency of the αSetup-AMG solver.

Unbounded inner product functional encryption from bilinear maps

Inner product functional encryption (IPFE) is one class of functional encryption supporting only inner product functionality. All previous IPFE schemes are bounded schemes, meaning that the vector length that can be handled in the scheme is fixed in the setup phase. In this paper, we propose the first unbounded IPFE schemes, in which we do not have to fix the lengths of vectors in the setup phase and can handle (a priori) unbounded polynomial lengths of vectors. Our first scheme is private-key based and fully function hiding. That is, secret keys hide the information of the associated function. Our second scheme is public-key based and provides adaptive security in the indistinguishability based security definition. Both our schemes are based on SXDH, which is a well-studied standard assumption, and secure in the standard model. Furthermore, our schemes are quite efficient, incurring an efficiency loss by only a small constant factor from previous bounded function hiding schemes.

Morphometric analysis of dental arch form changes in classII patients treated with clear aligners.

The purpose of this study was to evaluate the arch form changes in classII Caucasian patients treated with Invisalign® (Align Technology, San José, CA, USA). A total of 27classII patients, for whom amaximum of 4 mm arch expansion was planned, were selected. Both maxillary and mandibular digital casts were compared at three different times: pretreatment (T0), accepted set-up (T1), and retention phase (T2). Each digital model was imported into GOM Inspect© software (GOM GmbH, Braunschweig, Germany) to identify teeth crown facial axis (FA) and cusp points to create acoordinate system. In each model the origin of the coordinates was located at the contact point of central incisors and asystem of Cartesian axes was constructed. Using the FA points, an average arch form was obtained for each clinical step and then the following comparisons were performed for each class group: T0-T1, T0-T2, and T1-T2. T1 showed wider maxillary and mandibular dental arches compared to T0 with maximum movements observed in the premolar regions (maximum movement 1.94 mm for tooth15; P < 0.0001). In the T1-T2 comparison, amore buccal position of tooth22, tooth23, and tooth24 (maximum movement 0.56 mm; P < 0.05) and amore lingual position of tooth37 (maximum movement 0.81 mm; P < 0.01), tooth36, and tooth47 were observed at T1 with respect to T2. Although Invisalign® treatment resulted in asignificant increase in arch width according to the prescription, some of the outcomes were different than those planned especially in relation to the final position of the lower molars.

An efficient data integrity auditing protocol for cloud computing

Cloud data storage has brought a revolution in Information Technology (IT) sector with its inherent benefits like ease of use, cost efficiency, location independence etc. By outsourcing data on cloud servers, users can reduce the hardware expenditures and can simplify the data management process. A Cloud Service Provider (CSP) is responsible for offering secure data storage services. However, using these services in a secured manner and ensuring data integrity in these remote cloud servers remains to be an issue for the clients. In this paper, we propose an efficient approach for data integrity auditing in cloud computing. The objective of proposed protocol is to minimize the computational complexity of client during system setup phase of the auditing protocol. Based on the properties of bilinear pairings, the proposed protocol is publicly verifiable and supports dynamic operations on data. The security of proposed protocol relies on the stability of Computational Diffie Hellman Problem (CDHP) in a Random Oracle Model (ROM). Finally, the performance of proposed protocol has been evaluated by implementing a prototype of the protocol and conducting experiments. The results obtained have been compared with state-of-the-art protocols and demonstrates the high efficiency and adaptability of proposed protocol by clients with limited resources.

Characterizing marine ecosystems and fishery impacts using a comparative approach and regional food-web models

Conservation of marine ecosystems is important for the sustainable utilization of marine resources under environmental changes and anthropogenic impacts. In Japan, fishery management has been based on single-species stock assessments and little attempts have been made to assess and manage the impacts of multi-species multi-gear fisheries except for a few local case studies. In this study, we analyzed two local Ecopath food-web models constructed for Japanese waters (Northeastern Japan and Seto Inland Sea). We adopted a comparative approach to assess the ecosystem characteristics and fishery impacts through a comparison of these models with other published Ecopath models for various periods and regions of the world ocean. Among the metrics derived from Ecopath outputs, total biomass and total productivity represented the gross metabolic activities of the systems, and trophic level fractionation of biomass and catch reflected the trophic structure of the food webs and fishery removals. Loss of food for top predators, primary production required, and mean and variance of trophic level of catch were good indicators of the impacts and patterns of fishery removals. Our results illustrate the characteristics of food webs and fisheries in Japanese local ecosystems: The Northeastern Japan model showed the moderate productivity of the ecosystem, and fishery removals from lower and wider trophic levels. The Seto Inland Sea model indicated large impacts of fisheries on the low productivity neritic system. Such profiling of ecosystems and fisheries provides good overviews that will help initiate stakeholder discussion toward the set-up phase of ecosystem-based fishery management in Japan.

Performance Analysis of IoT System using Sleep Server Technique

Modern day living is driven by wireless sensor networks (WSN) as a component of ubiquitous technology. Internet of things (IoT) is a backbone network which enables the sensors to communicate with each other and with machines attached to that network through internet. The growing applications of system automation impose to usesr to use IoT. However, performance prediction turns out to be a challenge to manage the computing resources in IoT due to dynamic need of the user applications. We have proposed a model based on queueing theory approach for performance analysis of multi-server based IoT enabled system. We have proposed a M/M/C/Sleep where the working server is in OFF state when there is no data packets to serve and switched to ON through set-up phase if there are some data request for service. The performance indices are derived for cost analysis in terms of power consumption.

International league of associations for rheumatology recommendations for the management of psoriatic arthritis in resource-poor settings

BackgroundPsoriatic arthritis (PsA) is a challenging heterogeneous disease. The European League Against Rheumatism (EULAR) and the Group for Research and Assessment of Psoriasis and PsA (GRAPPA) last published their respective recommendations for the management of PsA in 2015. However, these guidelines are primarily based on studies conducted in resource replete countries and may not be applicable in countries in the Americas (except Canada and USA) and Africa. We sought to adapt the existing recommendations for these regions under the auspices of the International League of Associations for Rheumatology (ILAR).ProcessThe ADAPTE Collaboration (2009) process for guideline adaptation was followed to adapt the EULAR and GRAPPA PsA treatment recommendations for the Americas and Africa. The process was conducted in three recommended phases: set-up phase; adaptation phase (defining health questions, assessing source recommendations, drafting report), and finalization phase (external review, aftercare planning, and final production).ResultILAR recommendations have been derived principally by adapting the GRAPPA recommendations, additionally, EULAR recommendations where appropriate and supplemented by expert opinion and literature from these regions. A paucity of data relevant to resource-poor settings was found in PsA management literature.ConclusionThe ILAR Treatment Recommendations for PsA intends to serve as reference for the management of PsA in the Americas and Africa. This paper illustrates the experience of an international working group in adapting existing recommendations to a resource-poor setting. It highlights the need to conduct research on the management of PsA in these regions as data are currently lacking.Key Points• The paper presents adapted recommendations for the management of psoriatic arthritis in resource-poor settings.• The ADAPTE process was used to adapt existing GRAPPA and EULAR recommendations by collaboration with practicing clinicians from the Americas and Africa.• The evidence from resource-poor settings to answer clinically relevant questions was scant or non-existent; hence, a research agenda is proposed.

Novel efficient lattice-based IBE schemes with CPK for fog computing.

The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks. In this paper, firstly, we construct a novel efficient lattice-based IBE scheme with Combined Public Key (CPK) technique by keeping from consumptive trapdoor generation algorithm and preimage sampling algorithm, which is required by the existing lattice-based IBE schemes based on learning with errors (LWE). In addition, its key storage cost is lower and it is IND-ID-CPA secure in the random oracle model. Furthermore, based on this, an enhanced lattice-based IBE scheme with IND-ID-CCA security is developed by employing strong one-time signature. Our schemes only need O(n3/log n) additions of vectors, while the existing schemes need at least O(n3) of additions and multiplications in Setup and Extract phase.

Selfish node detection based on hierarchical game theory in IoT

Cooperation between nodes is an effective technology for network throughput in the Internet of Things. The nodes that do not cooperate with other nodes in the network are called selfish and malicious nodes. Selfish nodes use the facilities of other nodes of the network for raising their interests. But malicious nodes tend to damage the facilities of the network and abuse it. According to reviews of the previous studies, in this paper, a mechanism is proposed for detecting the selfish and malicious nodes based on reputation and game theory. The proposed method includes three phases of setup and clustering, sending data and playing the multi-person game, and update and detecting the selfish and malicious nodes. The process of setup and clustering algorithm are run in the first phase. In the second phase, the nodes of each cluster cooperate with each other in order to execute an infinite repeated game while forwarding their own or neighbor nodes’ data packets. In the third phase, each node monitors the operation of its neighbor nodes for sending the data packets, and the process of cooperation is analyzed for determining the selfish or malicious nodes which forwarded the data packets with delay or even not sent them. The other nodes reduce the reputation of the nodes which does not cooperate with them, and they do not cooperate with the selfish and malicious nodes, as punishment. So, selfish and malicious nodes are stimulated to cooperate. The results of simulation suggest that the detection accuracy of the selfish and malicious nodes has been increased by an average of 12% compared with the existing methods, and the false-positive rate has been decreased by 8%.

A Novel Reduced Sensing Time Routing Protocol in Cognitive Radio Networks

To avoid interference with primary users’ transmission in cognitive radio networks, the secondary users must perform a periodic spectrum sensing. The secondary users may cooperate with each other for sensing, in order to enhance the accuracy and reduce the overall spectrum sensing time. Spectrum sensing is also needed in other cognitive radio functions, such as in routing. Specifically, a cognitive radio node will not be able to route a packet to another cognitive radio node except if the channel is available on both nodes, where channels availability is determined by sensing. In this paper, a novel cooperative spectrum sensing protocol in ad-hoc cognitive radio networks is proposed in order to enhance routing performance. This approach is called Reduced Sensing Time Routing Protocol, in which a multi-hop path is found from a source to a destination. The route is composed of the intermediate cognitive radio nodes and the channel to be used at each hop. During the route setup phase, each intermediate node along the investigated path may select a neighbor node to perform channel sensing on its behalf, and this saved sensing time can be utilized to prolong the transmission time. As a result, the total end-to-end delay over the selected route is reduced. Simulation results show about 10% reduction in the overall average end-to-end delay as compared to conventional approach which assumes no cooperation exists in sensing between cognitive radio nodes.

SLEC: A Novel Serverless RFID Authentication Protocol Based on Elliptic Curve Cryptography

Internet of Things (IoT) is a new paradigm that has been evolving into the wireless sensor networks to expand the scope of networked devices (or things). This evolution drives communication engineers to design secure and reliable communication at a low cost for many network applications such as radio frequency identification (RFID). In the RFID system, servers, readers, and tags communicate wirelessly. Therefore, mutual authentication is necessary to ensure secure communication. Normally, a central server supports the authentication of readers and tags by distributing and managing the credentials. Recent lightweight RFID authentication protocols have been proposed to satisfy the security features of RFID networks. Using a serverless RFID system is an alternative solution to using a central server. In this model, both the reader and the tag perform mutual authentication without the need for the central server. However, many security challenges arise from implementing lightweight authentication protocols in serverless RFID systems. We propose a new secure serverless RFID authentication protocol based on the famous elliptic curve cryptography (ECC). The protocol also maintains the confidentiality and privacy of the messages, tag information, and location. Although most of the current serverless protocols assume secure channels in the setup phase, we assume an insecure environment during the setup phase between the servers, readers, and tags. We ensure that the credentials can be renewed by any checkpoint server in the mobile RFID network. Thus, we implement ECC in the setup phase (renewal phase), to transmit and store the communication credentials of the server to multiple readers so that the tags can perform the mutual authentication successfully while far from the server. The proposed protocol is compared with other serverless frameworks proposed in the literature in terms of computation cost and attacks resistance.

Culture as a Sensor? A Novel Perspective on Human Activity Recognition

Human Activity Recognition (HAR) systems are devoted to identifying, amidst the sensory stream provided by one or more sensors located so that they can monitor the actions of a person, portions related to the execution of a number of a-priori defined activities of interest. Improving the performance of systems for Human Activity Recognition is a long-standing research goal: solutions include more accurate sensors, more sophisticated algorithms for the extraction and analysis of relevant information from the sensory data, and the enhancement of the sensory analysis with general or person-specific knowledge about the execution of the activities of interest. Following the latter trend, in this article we propose the association and enhancement of the sensory data analysis with cultural information, that can be seen as an estimate of person-specific information, relieved of the burden of a long/complex setup phase. We propose a culture-aware Human Activity Recognition system which associates the recognition response provided by a state-of-the-art, culture-unaware HAR system with culture-specific information about where and when activities are most likely performed in different cultures, encoded in an ontology. The merging of the cultural information with the culture-unaware responses is done by a Bayesian Network, whose probabilistic approach allows for avoiding stereotypical representations. Experiments performed offline and online, using images acquired by a mobile robot in an apartment, show that the culture-aware HAR system consistently outperforms the culture-unaware HAR system.

Energy efficient protocol for routing and scheduling in wireless body area networks

An energy efficient protocol for routing and scheduling in wireless body area networks is proposed by considering node energy, path loss, traffic type, and other relevant factors. First, a channel competition procedure is proposed to reduce network flooding, making use of the related information that sensor nodes collected in the initialization phase. Then, energy abundant routes are selected according to the node type and energy consumption in routing setup phase. Finally, an adaptive slot assignment method is designed to meet requirements of data rate and priority in slot assignment phase. The simulation results show that the proposed protocol not only save energy, but also improve the channel utilization.

Flexible attribute-based proxy re-encryption for efficient data sharing

An increasing number of people are sharing their data through third-party platforms. Attribute-based encryption (ABE) is a promising primitive that allows enforcing fine-grained access control on the data to be shared. An issue in ABE is that a priori access policies should be determined during the system setup or encryption phase, but these policies will become obsolete over time. Another issue is that the decryption of ABE generally requires complicated and expensive computations, which may be unaffordable for resource-limited users (e.g., mobile-device users). To address these issues, we propose a new paradigm called hybrid attribute-based proxy re-encryption (HAPRE). In HAPRE, a semitrusted proxy can be authorized to convert ciphertexts of an ABE scheme into ciphertexts of an identity-based encryption (IBE) scheme without letting the proxy know the underlying messages. With these features, HAPRE enables resource-limited users to efficiently access the data previously encrypted by ABE. We construct two HAPRE schemes by utilizing a compact IBE scheme and a key rerandomization technique, and then we prove that the schemes are semantically secure and collusion resistant. Theoretical and experimental analyses demonstrate the efficiency of the HAPRE schemes.

A survey on zero knowledge range proofs and applications

In last years, there has been an increasing effort to leverage distributed ledger technology (DLT), including blockchain. One of the main topics of interest, given its importance, is the research and development of privacy mechanisms, as for example is the case of zero knowledge proofs (ZKP). ZKP is a cryptographic technique that can be used to hide information that is put into the ledger, while still allowing to perform validation of this data. In this work we describe different strategies to construct zero knowledge range proofs (ZKRP), as for example the scheme proposed by Boudot (in: Bart (ed) Advances in cryptology—EUROCRYPT 2000, Springer, Berlin, 2000) in 2001; the one proposed by Camenisch et al. (in: Josef (ed) Advances in cryptology—ASIACRYPT 2008, Springer, Berlin, 2008), and bulletproofs (Bunz et al., in: 2018 IEEE symposium on security and privacy (SP), 2018), proposed in 2017. We also compare these strategies and discuss possible use cases. Since bulletproofs (Bunz et al. 2018) is the most efficient construction, we will give a detailed description of its algorithms and optimizations. Bulletproofs is not only more efficient than previous schemes, but also avoids the trusted setup, which is a requirement that is not desirable in the context of DLT and blockchain. In case of cryptocurrencies, if the setup phase is compromised, it would be possible to generate money out of thin air. Interestingly, bulletproofs can also be used to construct generic ZKP, in the sense that it can be used to prove generic statements, and thus it is not only restricted to ZKRP, but it can be used for any kind of proof of knowledge. Hence Bulletproofs leads to a more powerful tool to provide privacy for DLT. Here we describe in detail the algorithms involved in Bulletproofs protocol for ZKRP. Also, we present our implementation, which was open sourced (Morais et al., in: Zero knowledge range proof implementation, 2018. https://github.com/ing-bank/zkrangeproof ).

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

In this paper, we propose a novel deep learning and blockchain-based energy framework for smart grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a blockchain-based scheme and a deep learning-based scheme. The blockchain-based scheme consists of five phases: setup phase, agreement phase, creating a block phase and consensus-making phase, and view change phase. It incorporates a novel reliable peer-to-peer energy system that is based on the practical Byzantine fault tolerance algorithm and it achieves high throughput. In order to prevent smart grid attacks, the proposed framework makes the generation of blocks using short signatures and hash functions. The proposed deep learning-based scheme is an intrusion detection system (IDS), which employs recurrent neural networks for detecting network attacks and fraudulent transactions in the blockchain-based energy network. We study the performance of the proposed IDS on three different sources the CICIDS2017 dataset, a power system dataset, and a web robot (Bot)-Internet of Things (IoT) dataset.

The Applicability of Attardo's Set-up, Incongruity, Resolution Model on Unintentional Incongruities

The present paper aims at investigating the flexibility of Attardo's model, Set-up, Incongruity, Resolution (hence SIR) in the analysis of a selection of unintentional humorous instances taken from the British sitcom 'Mind Your Language'. Accordingly, this model has been used in the analysis of three randomly selected discourses from a number of representative cases, where the total number of instances in these three selected discourses reached (11). Results have revealed that the set-up phase can be the same to a series of incongruities and resolutions that occur within the same discourse. Moreover, what is incongruous to one might not be so to another. Further, the resolution is not the end of the problem; it might lead to another incongruity. Finally, Attardo's model can be applicable with unintentional humor; however, the number of phases might not always be three; their sequence might not be the same; the reactions are different; the length of each phase also differs; and the resolution phase might be a base of a new incongruity

Hierarchical matrix arithmetic with accumulated updates

Hierarchical matrices can be used to construct efficient preconditioners for partial differential and integral equations by taking advantage of low-rank structures in triangular factorizations and inverses of the corresponding stiffness matrices. The setup phase of these preconditioners relies heavily on low-rank updates that are responsible for a large part of the algorithm’s total run-time, particularly for matrices resulting from three-dimensional problems. This article presents a new algorithm that significantly reduces the number of low-rank updates and can shorten the setup time by 50% or more.

Finite Strain Homogenization Using a Reduced Basis and Efficient Sampling

The computational homogenization of hyperelastic solids in the geometrically nonlinear context has yet to be treated with sufficient efficiency in order to allow for real-world applications in true multiscale settings. This problem is addressed by a problem-specific surrogate model founded on a reduced basis approximation of the deformation gradient on the microscale. The setup phase is based upon a snapshot POD on deformation gradient fluctuations, in contrast to the widespread displacement-based approach. In order to reduce the computational offline costs, the space of relevant macroscopic stretch tensors is sampled efficiently by employing the Hencky strain. Numerical results show speed-up factors in the order of 5–100 and significantly improved robustness while retaining good accuracy. An open-source demonstrator tool with 50 lines of code emphasizes the simplicity and efficiency of the method.