Discovery Scheme Research Articles

Automatic Buffer Overflow Warning Validation

Static buffer overflow detection techniques tend to report too many false positives fundamentally due to the lack of software execution information. It is very time consuming to manually inspect all the static warnings. In this paper, we propose BovInspector, a framework for automatically validating static buffer overflow warnings and providing suggestions for automatic repair of true buffer overflow warnings for C programs. Given the program source code and the static buffer overflow warnings, BovInspector first performs warning reachability analysis. Then, BovInspector executes the source code symbolically under the guidance of reachable warnings. Each reachable warning is validated and classified by checking whether all the path conditions and the buffer overflow constraints can be satisfied simultaneously. For each validated true warning, BovInspector provides suggestions to automatically repair it with 11 repair strategies. BovInspector is complementary to prior static buffer overflow discovery schemes. Experimental results on real open source programs show that BovInspector can automatically validate on average 60% of total warnings reported by static tools.

InPPTD: A Lightweight Incentive-Based Privacy-Preserving Truth Discovery for Crowdsensing Systems

Recently, truth discovery in crowdsensing systems has received considerable attention with its appealing features for extracting truthful information from multiple unreliable data sources. However, it also poses new challenges to the issues of privacy and security. On the one hand, workers’ sensed data can be used to infer their privacy. On the other hand, workers may be selfish and lazy, especially in the Internet-of-Things environment, devices are usually resource constrained, so they may dishonestly execute the costly sensing task so as to reduce resource consumption, or even break the protocol to obtain illegal rewards. Although some privacy-preserving truth discovery schemes have been proposed, they still cannot achieve strong privacy protection while keeping efficiency on the worker side, and still has no efficient incentive mechanism to persuade workers to participate in the system operations. In this article, we propose an incentive-based privacy-preserving truth discovery framework, named InPPTD. By adopting the Paillier homomorphic cryptosystem and two noncolluding servers, InPPTD not only effectively protects workers’ sensed data information but also preserves the privacy of these workers’ weight information. Meanwhile, a weight-based incentive mechanism is introduced in InPPTD to reduce the number of lazy workers. Security and performance analysis shows that InPPTD can guarantee stronger security features, while also ensure efficiency in terms of computation and communication overhead.

Neighbor Discovery Based on Cross-Technology Communication for Mobile Applications

Neighbor discovery is essential for mobile devices to find each other before communications. Traditional discovery protocols are constrained by the in-technology communication paradigm. Cross-Technology Communications enable ZigBee nodes to be coordinated by a WiFi node without any hardware changes or gateway equipment, which sheds the light on more efficient neighbor discovery schemes. In this work, we introduce a new direction for neighbor discovery based on Cross-Technology Communication technique and propose a physical-layer technique called NewBee. NewBee takes the advantages of coordinations from a WiFi node to assist ZigBee nodes for neighbor discovery. A simple yet effective Countdown Mechanism is employed so that ZigBee nodes are coordinately waked up according to the beacons in the Countdown Mechanism. We give a rigorous analysis on NewBee and the worst-case discovery latency is significantly reduced from the traditional $O(n^2)$ to $O(n)$ , where $n$ indicates the reciprocal of node's duty-cycle. The experimental results show that NewBee only needs 11%, 10%, 2.5%, 2.4% and 4.4% percent of discovery time compared with the state-of-the-art BlindDate, SearchLight, Disco, Quorum, and Birthday protocols with 5% nodes’ duty-cycle, respectively.

Directional neighbor discovery in mmWave wireless networks

The directional neighbor discovery problem, i.e., spatial rendezvous, is a fundamental problem in millimeter wave (mmWave) wireless networks, where directional transmissions are used to overcome the high attenuation. The challenge is how to let the transmitter and the receiver beams meet in space under deafness caused by directional transmission and reception, where no control channel, prior information, and coordination are available. In this paper, we present a Hunting-based Directional Neighbor Discovery (HDND) scheme for ad hoc mmWave networks, where a node follows a unique sequence to determine its transmission or reception mode, and continuously rotates its directional beam to scan the neighborhood for other mmWave nodes. Through a rigorous analysis, we derive the conditions for ensured neighbor discovery, as well as a bound for the worst-case discovery time and the impact of sidelobes. We validate the analysis with extensive simulations and demonstrate the superior performance of the proposed scheme over several baseline schemes.

Dynamic discretization discovery for the service network design problem with mixed autonomous fleets

We consider a service network design problem for the tactical planning of parcel delivery in a city logistics setting. A logistics service provider seeks a repeatable plan to transport commodities from distribution centers on the periphery to inner-city satellites. In a heterogeneous infrastructure, autonomous vehicles in level 4 may only drive in feasible streets but need to be pulled elsewhere by manually operated vehicles in platoons. We formulate an integer program to determine the fleet mix, schedule transportation services, and decide on the routing or outsourcing of commodities. Platooning requires a high level of synchronization between vehicles which demands the time-expanded networks to contain narrow time intervals. Thus, we develop an algorithm based on the dynamic discretization discovery scheme which refines partially time-expanded networks iteratively without having to enumerate the fully time-expanded network a priori. We introduce valid inequalities and provide two enhanced versions of the algorithm that exploit linear relaxations of the problem. Further, we propose heuristic ideas to speed up the search for high-quality solutions. In a computational study, we analyze the efficacy of the algorithm in different versions and observe improvements of computational performance in comparison to a commercial solver. Finally, we solve a case study on a real-world based network to obtain insights into the deployment of a mixed autonomous fleet in an existing heterogeneous infrastructure.

Efficient construction of linear models in materials modeling and applications to force constant expansions

Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science.

Cyclic Block-Spreading Direct Discovery for D2D Networks

Device-to-device (D2D) communications has become a pivotal piece to complete the puzzle of future generation of wireless communication systems. The enabling technology for the D2D communications lies in its discovery mechanism. Plenty of works have been proposed to enhance the efficiency of direct D2D discovery without network assistance. Generally, the main idea of these works is to avoid unnecessary collisions during the discovery period. However, owing to the limited resources for D2D discovery, the effectiveness of the collision avoidance based schemes is always restricted. Instead, we aim to make the discovery beacon robust against collisions. To achieve this, the cyclic block spreading (CBS) scheme is proposed to spreading the beacon over multiple subframes without widening the bandwidth and the need of redesigning the beacon. That means that solely minor modification of specification is required. The simulation and analytic results show that using the CBS direct discovery scheme, the performance in terms of outage probability, collision rate and time periods for complete discovery can be significantly improved.

An Indirect Controller-Legacy Switch Forwarding Scheme for Link Discovery in Hybrid SDN

Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%-97.9% in the number of ports detected and reduction in number of messages of about 25%-58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

Dynamic routing discovery scheme for high mobility in mobile ad hoc wireless networks

An innovative technology that is widely used in many applications is the Mobile Ad-hoc Network (MANET). Discovery and maintenance of routes at MANET are important issues. Within MANET, broadcasting is used to discover a path within on-demand routing protocols. Establishing and maintaining a route periodically among the nodes is the challenge that requires the transmitting of control packets across a network. This state leads to the issue of broadcasting storms. Broadcasting control packets increase control packets overhead and decrease network performance. In this paper, we proposed a scheme called AODV-Velocity and Dynamic (AODV-VD) for effective broadcast control packets. The routing protocol for the ad-hoc on-demand distance victor (AODV) is used to implement the proposed AODV-VD scheme. AODV-VD scheme reduces both the excessive route discovery control packets and network overhead. Network simulator version 2.35 (NS2.35) was used to compare the proposed AODV-VD scheme to the AODV routing protocol in terms of end-to-end latency, average throughput, packet transmission ratio and overhead ratio.

Bat echolocation-based algorithm for device discovery in D2D communication

Proximal device discovery is an essential initial phase in the installment of a device-to-device communication system in cellular networks. Therefore, an efficient device discovery scheme must be proposed with characteristics of minimum latency, discover maximum devices, and energy-efficient discovery in dense areas. In this paper, a bat echolocation-based algorithm derived from the bat algorithm is proposed and analyzed to fulfill the requirement of a proximal device discovery procedure for the cellular networks. The algorithm is applied to multiple hops and cluster devices when they are in a poor coverage zone. In this proposed algorithm, devices are not required to have prior knowledge of proximal devices, nor device synchronization is needed. It allows devices to start discovering instantly at any time and terminate the proximal device discovery session on completion of the discovery of the required proximal devices. Finally, device feedback is utilized to discover the hop devices in the clusters and analyze proximal discovery in a multi-hop setting. Along with this, a random device mobility pattern is defined based on human movement, and the device discovery algorithm is applied. The device discovery probability is calculated based on the contact duration and meeting time of the devices. We set up an upper bound less than 10 ms in long-term evolution of running time of the bat echolocation-based algorithm; this upper bound signifies the maximum degree of device discovery (more than 75% of the system) and the total number of devices. The outcomes thus imply that the proposed bat echolocation-based algorithm upper bound is better than 10 ms.

A novel scheme for essential protein discovery based on multi-source biological information

Mining essential protein is crucial for discovering the process of cellular organization and viability. At present, there are many computational methods for essential proteins detecting. However, these existing methods only focus on the topological information of the networks and ignore the biological information of proteins, which lead to low accuracy of essential protein identification. Therefore, this paper presents a new essential proteins prediction strategy, called DEP-MSB which integrates a variety of biological information including gene expression profiles, GO annotations, and Domain interaction strength. In order to evaluate the performance of DEP-MSB, we conduct a series of experiments on the yeast PPI network and the experimental results have shown that the proposed algorithm DEP-MSB is more superior to the other existing traditional methods and has obviously improvement in prediction accuracy.

An efficient and privacy-preserving truth discovery scheme in crowdsensing applications

Truth discovery is a reliable and effective technique to resolve conflicts of heterogeneous data and estimate user reliability in mobile crowdsensing systems. Despite its effectiveness, the widespread adoption of truth discovery requires solid privacy preservation against users’ sensory data and reliability information. Existing works of private truth discovery are primarily based on conventional cryptographic primitives, which introduce tremendous workloads on the system. In this work, we first propose an efficient and privacy-preserving truth discovery framework (EPTD-I) by adopting a novel data perturbation mechanism. EPTD-I not only protects users’ privacy but also introduces little overhead on the user side. Moreover, for high mobility environments, we improve the design with a user non-interactive scheme named EPTD-II to shift all encrypted truth discovery operations to cloud platforms. In EPTD-II, each user’s sensitive information is also kept private during the complete truth discovery procedure. Thorough security analysis demonstrates that our proposed schemes are secure and offer a high level of privacy preservation. Extensive experiments conducted on practical and simulated crowdsensing applications demonstrate the effectiveness and efficiency of the proposed schemes.

A Proposed Scheme for Fault Discovery and Extraction Using ANFIS: Application to Train Braking System

This paper showcases the use of model oriented techniques for real time fault discovery and extraction on train track unit. An analytical system model is constructed and simulated in Mathlab to showcase the fair and unfair status of the system. The discovery and extraction phases are centered on a hybrid adaptive neuro-fuzzy inference feature extraction and segregated module. Output module interprites zero (0) as a good status of the traintrack unit and one (1) as an unpleasant status. Final results showcase the robustness and ability to discover and extract multitude of unpleasant scenarios that hinder the smooth operations of train track units due to its high selectivity and sensitivity quality.

Truth Discovery in Big Data Social Media Sensing Applications

The detection of truthful information amid data provided by online social media platforms (e.g., Twitter, Facebook, Instagram) is a critical task in the trend of big data. Truth Discovery is nothing but the extraction of true information or facts from unwanted and raw data, which has become a difficult task nowadays in today's day and age due to the rampant spread of rumors and false information. Before posting anything on the social media platform, people do not consider fact-checking and the source authenticity and frantically spread them by re-posting them which has made the detection of truthful claims more difficult than ever. So, this problem needs to be addressed soon since the impact of false information and misunderstanding can be very powerful and misleading. This mission, truth discovery, is targeted at establishing the authenticity of the sources and therefore the truthfulness of the statements that they create without knowing whether it is true or not. We propose a Big Data Truth Discovery Scheme (BDTD) to overcome the major problems. We have three major problems, the main one being "False information spread" where a large number of sources lead to false or fake statements, making it difficult to distinguish true statements, now this problem is solved by our scheme by studying the various behaviors of sources. On Twitter for example rumormongering is common. The second problem is "lack of claims" where most outlets contribute only a tiny small number of claims, giving very few pieces of evidence and making it not sufficient to analyze the trustworthiness of such sources, this problem is addressed by our scheme where it uses an algorithm that evaluates the claim’s truthfulness and historic contributions of the source regarding the claim. Thirdly the scalability challenge, due to the clustered design of their existing truth discovery algorithms, many existing approaches don't apply to Big-scale social media sensing cases so this challenge is managed by our scheme by making use of frameworks HTCondor and Work Queue. This scheme computes both the reliability of the sources and, ultimately, the legitimacy of statements using a novel approach. A distributed structure is also developed for the implementation of the proposed scheme by making use of the Work Queue (platform) in the HTCondor method (maybe distributed). Findings of the test on a real-world dataset indicate that the BDTD system greatly outperforms the existing methods of Discovery of Truth both in terms of performance and efficiency.

Emission Wavelength Switchable Carbon Dots Combined with Biomimetic Inorganic Nanozymes for a Two-Photon Fluorescence Immunoassay

In this work, o-phenylenediamine is utilized as a precursor to synthesize the fluorescent emission wavelength switchable carbon dots (o-CDs). Our investigation reveals that ferrous ions (Fe2+) can effectively induce fluorescence quenching of o-CDs by chelation and aggregation. After the addition of hydrogen peroxide (H2O2), the fluorescence of o-CDs recovers and the fluorescent color changes from yellow to green. As far as we know, o-CDs are the first reported CDs with switchable fluorescence emission wavelength. In order to fabricate an enzyme-free immunosensor, an amino-functionalized dendritic mesoporous silica nanoparticle (DMSN)-gold nanoparticle (Au NP) nanostructure was fabricated as a glucose oxidase mimetic nanoenzyme by in situ coating of the Au NPs on the surface of the DMSNs. Then, the functionalized DMSN-Au NPs were modified on the detection antibody and hydrolyzed with glucose to produce H2O2. This immune induced recognition strategy combines with the o-CDs+Fe2+ signal generation system to achieve specific and sensitive detection of the target. The replacement of glucose oxidase by DMSN-Au NPs not only reduces the cost but also provides significantly amplified signals due to DMSNs haing a high specific surface area. We show the detection of carcinoembryonic antigen (CEA) as an example target to evaluate the analytical figure of merits of the proposed strategy. Under the optimal conditions, two-photon-based o-CDs displayed excellent performances for CEA and the limit of detection as low as 74.5 pg/mL with a linear range from 0.1 to 80 ng/mL. The proposed fluorescent immunosensor provides an optional and potential scheme for low cost, high sensitivity, and versatile discovery of clinical biomarkers.

Sector Scanning Algorithm (SSA) for device discovery in D2D communication

ABSTRACT A device-to-device (D2D) communication permits wireless devices to communicate mutually without network assistance. It promises to enhancement in spectral and network efficiency in the next-generation networks. A D2D communication needs device discovery, which is considered as a primary task. It represents the geographic coordinates of the wireless devices in a cellular network. If the device discovery is not legitimated properly may present noteworthy latency, energy consumption, and inaccuracy. In this work, a novel device discovery procedure based on cell sector scanning algorithm (SSA) is proposed for in-band D2D communication and the device discovery is performed under two scenarios: haphazard walk and velocity. An experimental setup is established to receive signal strength (RSS) measurements in a specific sector. The coverage area is divided into eight sectors to discover the devices. The sectors are used to apply and verify the proposed solution and the algorithm for device discovery. It considers the existing radio resources for proximal device discovery and adjusts the discovery signal transmission appropriately. The discovery algorithm must be fast, accurate, and energy efficient at the device level and network level. This technique performs well and enhances the quality of discovery process more than 20% of the linear estimation and diminishes the energy consumption by 24% to accomplish the discovery in dense areas and makes it dynamic and adjustable for changing environments. A mathematical analysis based on the sector scanning attempts is also provided. Furthermore, the improved discovery scheme is evaluated and confirmed by the probability of isolation.

Performance Improvement in Conventional AODV Using Energy Efficient Manifold Route Discovery Scheme

The decentralized network formed without any assistance of the fixed infrastructure and just by the employing the volunteering mobile or the portable devices that work in a cooperative manner along with the other devices that are clubbed in the network are inbuilt with the capacity of providing a best data conveyance facility utilizing the unicast, multicast and the broadcast methods of the routing strategies. Despite the capabilities of these type of networks, it faces difficulties in devising a strategy of routing according to the dynamic nature of the distributed network. The reactive type or the on demand routing are highly used over the proactive way of routing in the decentralized network as the performance of the former is better compared to the latter. As the mobile device employed are capable are moving from its position irrespective of time, and they behave as the route and as well as the end node or the initiating node. The important factors to be considered while forming the route is the power required for transmission and the current position of the nodes, this is quiet tedious. The study in the paper provides the importance of the on demand routing distance vector routing for the distributed network that are linked in a adhoc manner and analysis all the existing on demand distance vector routing strategies and form a new manifold route discovery scheme to improve the conventional_AODV. The method devised to improve the conventional_AODV proves to be efficient when simulated using the NS-3 on the basis of the packet delivery rate, load balancing and the energy usage.

Rock, Paper, Scissors Game Based Model for Content Discovery in P2P MANETs

Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network communicate with each other forming one-or-more overlay structure. And, thus the peer’s connections in such overlay networks plays a crucial role. To harness this, we propose a model that focuses on the underlying network topology as each virtual link of the overlay network is supported by a path in the underlying physical network. Also, we design a new resource discovery algorithm that uses the famous Rock-Paper-Scissors (RPS) game ideology. In this work, we present a Rock-Paper-Scissors-Game-Based (RPSGB) algorithm for content discovery in mobile peer-to-peer network. The proposed work focuses on providing efficient resource discovery scheme in such a dynamic network, using the game theory concepts. Our scheme is a light-weight model aimed to suit the unstructured architecture better. The major goal of this work, is to reduce the consumption of power, minimize the lookup cost, lower the bandwidth consumption, and increase the hit ratio. We compare our scheme with the traditional well-known benchmarked schemes. After evaluation, the simulation results justify the effectiveness of the proposed protocol. The obtained results show that the proposed scheme substantially decreases the network traffic, lowers the battery power and bandwidth consumption, while having good search efficiency. Also, the search latency is minimized. The result justifies that RPSGB algorithm proposes to make resource searching much more efficient, and improves the statistics against the posed challenges.

Distributed finite‐time information discovery‐based secondary restoration for islanded microgrids

A new finite-time secondary voltage and frequency restoration scheme for an islanded microgrid (MG) is proposed in this study. All distributed generators (DGs) do not have access to the voltage and frequency reference value which makes their control more challenging. Therefore, a finite-time Information Discovery Scheme (IDS) is incorporated into the distributed secondary control scheme. The IDS provides local estimates of the global references of voltage and frequency for DGs in finite-time, and distributed control scheme uses this information to restore the voltage and frequency of MG to their reference values while accurately sharing the active power among the DGs in finite-time. The proposed control scheme is implemented on a sparse communication network and obviates the need for a central controller thereby reducing the risk of single-point-failure. The proof for finite-time restoration and restoration time upper-bound are derived using Lyapunov theory. The effectiveness of the proposed control scheme is verified through simulation of an MG test system in MATLAB/SimPowerSystem environment for sudden load variation and time-varying communication topology. The proposed control scheme supports plug and play capability and exhibits better convergence performance and disturbance rejection than the asymptotic controller based on neighbourhood tracking error.

Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems

Billions of sensing devices have been connected to the Internet of Things (IoT), generating a large volume of data that can be turned into valuable insights for many applications. Location information is critical for many IoT applications. However, most sensor devices are randomly deployed and locations are unknown. Thus, it is a challenging issue to discover the physical topology of the IoT system consisted of thousands of low-cost sensor devices. In this paper, a Vehicles joint UAVs Topology Discovery (VUTD) scheme is proposed that can discover the physical topology with low-cost and accuracy. There are two main steps in VUTD scheme: (1) Vehicles are used as mobile anchors to assist adjacent sensor devices in positioning. They are also used to collect logical topology information of the IoT system. The collected logical topology information and location information can be combined into physical topology information that will be sent to the cloud platform through vehicles. (2) The cloud platform analyzes the received information to determine the area where the physical topology discovery is not completed. Then, the cloud platform dispatches the UAV as a flight anchor to locate these points. Experiments based on realworld taxi trajectory are conducted to verify the effectiveness of VUTD scheme. The experimental results show that the VUTD scheme has better performance. Compared with the VTD scheme, the localization ratio is increased by up to 13.6%, and the mean localization error is reduced by up to 90.78%. Compared with UTD, the cost of location discovery is reduced by up to 77.7%.