Eaodv: A*-based Enhancement Ad-hoc on Demand Vector Protocol to Prevent Black Hole Attacks

Black hole attack is an attack where a node that responds to RREQ from the source node by replying a fake freshness information and false hop count. The black hole nodes do not respond to distributed co-operation in routing protocol to absorb all the packets, as a result, the network performance will drop. Most previous works are focused on anomaly detection through dynamic trusted of the neighbouring nodes. We find out that the internal comparisons take a long time. This loss can be shortened by changing the routing mechanism. We propose an enhancement of AODV protocol, named EAODV, that is able to prevent black hole attacks. The EAODV can find a shortest path of routing discovery using A* heuristic search algorithm. Values of hop count and estimate time to reach the destination node are used as input in the heuristic equation and one-way hash function is used to make a secure value and then to casting it to all neighbouring nodes. Experiments were conducted in NS2 to simulate EAODV in different running time with and without black hole nodes. The EAODV performance results are indicated better in terms Packet loss and Average End-to-End delay

Optimizing physical protection system using domain experienced exploration method

Assessing physical protection system efficiency is mostly done manually by security experts due to the complexity of the assessment process and lack of tools. Computer aided numerical vulnerability analysis has been developed to quantitatively assess physical protection systems. A variety of methods have been proposed to optimize physical protection systems, where one of the most advanced approaches entails precisely defining which security components should be selected and where they should be placed at protected facilities, taking into consideration adversary type, to maximize the probability that an adversary will be stopped at minimum system cost. The most computationally intensive part of the optimization process is the evaluation. The evaluation involves recreating search space and finding optimal adversary’s attack paths from each entry point. We present the domain experienced exploration method that optimizes evaluation process during the search for optimum solutions, considering results from previous evaluations. Performed experiments show that using the presented method, in real-world domains, results in a reduction of evaluation iterations

Cross-Company Routing Planning: Determining Value Chains in a Dynamic Production Network Through a Decentralized Approach

Demand-based, local production will gain relevance in the context of sustainability and circular economy. One way to implement local value creation is through establishing highly dynamic networks that consolidate the competencies of regional manufacturers. Consequently, the structure of the value chains needs to be determined ad hoc dependent on demand. This is a rather challenging task due to the dynamics within such networks and the flat hierarchies. Traditionally, value chains are defined and controlled in a centralized form by a lead firm or a separate stakeholder (e.g. Intermediary, Broker). However, to accommodate the dynamics of demand and the increasing complexity of products, we propose a decentralized form of coordination. The basic idea is to upscale Routing Planning, used in Process Planning, to a network level. Meaning instead of a centralized instance within a company defining the production steps, the stakeholders will collaboratively determine the cross-company Routing Plan, effectively building the value chain. Thus, the accumulated experience and knowledge of all stakeholders can be utilized to efficiently fulfil current customer demand, since the value chain will be executed by the same stakeholders that created it. But in order to coordinate the sequencing of operations by multiple stakeholders, suitable methods need to be implemented. We look at a strategy to facilitate such a collaboration between companies and demonstrate one possible technical implementation based on AI planning using Planning Domain Definition Language (PDDL)

Dviejų lygių iteracinis tabu paieškos algoritmas kvadratinio paskirstymo uždaviniui

 In this paper, a 2-level iterated tabu search (ITS) algorithm for the solution of the quadratic assignment problem (QAP) is considered. The novelty of the proposed ITS algorithm is that the solution mutation procedures are incorporated within the algorithm, which enable to diversify the search process and eliminate the search stagnation, thus increasing the algorithm’s efficiency. In the computational experiments, the algorithm is examined with various implemented variants of the mutation procedures using the QAP test (sample) instances from the library of the QAP instances – QAPLIB. The results of these experiments demonstrate how the different mutation procedures affect and possibly improve the overall performance of the ITS algorithm.Šiame straipsnyje nagrinėjamas vadinamasis dviejų lygių iteracinis tabu paieškos (ITP) algoritmas kvadratinio paskirstymo (KP) uždaviniui. Algoritmo naujumas yra tas, jog į jį yra integruotos sprendinių mutavimo procedūros, kurių esminė paskirtis yra diversifikuoti paieškos procesą, išvengiant paieškos stagnacijos ir taip padidinant jos efektyvumą. Algoritmo veikimas išbandytas su įvairių tipų mutavimo procedūrų realizavimo variantais. Atlikti kompiuteriniai eksperimentai su KP uždavinio testavimo duomenų pavyzdžiais iš standartinių pavyzdžių bibliotekos QAPLIB. Pateikti eksperimentų rezultatai, kurie iliustruoja, kaip skirtingos prigimties mutavimo procedūros, esančios ITP sudėtyje, gali įvairiai paveikti algoritmo efektyvumą

Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO)

PURPOSE: To develop a rapid and accurate MRI phase-unwrapping technique for challenging phase topographies encountered at high magnetic fields, around metal implants, or postoperative cavities, which is sufficiently fast to be applied to large-group studies including Quantitative Susceptibility Mapping and functional MRI (with phase-based distortion correction). METHODS: The proposed path-following phase-unwrapping algorithm, ROMEO, estimates the coherence of the signal both in space-using MRI magnitude and phase information-and over time, assuming approximately linear temporal phase evolution. This information is combined to form a quality map that guides the unwrapping along a 3D path through the object using a computationally efficient minimum spanning tree algorithm. ROMEO was tested against the two most commonly used exact phase-unwrapping methods, PRELUDE and BEST PATH, in simulated topographies and at several field strengths: in 3T and 7T in vivo human head images and 9.4T ex vivo rat head images. RESULTS: ROMEO was more reliable than PRELUDE and BEST PATH, yielding unwrapping results with excellent temporal stability for multi-echo or multi-time-point data. It does not require image masking and delivers results within seconds, even in large, highly wrapped multi-echo data sets (eg, 9 seconds for a 7T head data set with 31 echoes and a 208 × 208 × 96 matrix size). CONCLUSION: Overall, ROMEO was both faster and more accurate than PRELUDE and BEST PATH, delivering exact results within seconds, which is well below typical image acquisition times, enabling potential on-console application

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation