Impact of Denial-of-Service Attack on Directional Compact Geographic Forwarding Routing Protocol in Wireless Sensor Networks

يَعِدُ بروتوكول التوجيه، الموجه الجغرافي المضغوط (DCGF) بتوليد الحد الأدنى من النفقات العامة من خلال استخدام هوائي ذكي وتجميع واعٍ لجودة الخدمة (QoS). ومع ذلك، تم اختبار DCGF فقط في سيناريو خالٍ من الهجمات دون إشراك عناصر الأمان.  لذلك، تم إجراء استقصاء لفحص خوارزمية بروتوكول التوجيه فيما إذا كانت آمنة ضد الشبكات القائمة على الهجوم بوجود هجوم رفض الخدمة (DoS).  تم إجراء هذا التحليل على هجوم DoS باستخدام مهاجم واحد مثالي، A1، للتحقيق في تأثير هجوم DoS على DCGF في خط اتصال.  أظهرت الدراسة أن   DCGF لا يعمل بكفاءة من حيث نسبة تسليم الحزم واستهلاك الطاقة حتى على مهاجم واحد.Directional Compact Geographic Forwarding (DCGF) routing protocol promises a minimal overhead generation by utilizing a smart antenna and Quality of Service (QoS) aware aggregation. However, DCGF was tested only in the attack-free scenario without involving the security elements. Therefore, an investigation was conducted to examine the routing protocol algorithm whether it is secure against attack-based networks in the presence of Denial-of-Service (DoS) attack. This analysis on DoS attack was carried out using a single optimal attacker, A1, to investigate the impact of DoS attack on DCGF in a communication link. The study showed that DCGF does not perform efficiently in terms of packet delivery ratio and energy consumption even on a single attacker

A Trust-Region Algorithm with Adaptive Stochastic Collocation for PDE Optimization under Uncertainty

The numerical solution of optimization problems governed by partial differential equations (PDEs) with random coefficients is computationally challenging because of the large number of deterministic PDE solves required at each optimization iteration. This paper introduces an efficient algorithm for solving such problems based on a combination of adaptive sparse-grid collocation for the discretization of the PDE in the stochastic space and a trust-region framework for optimization and fidelity management of the stochastic discretization. The overall algorithm adapts the collocation points based on the progress of the optimization algorithm and the impact of the random variables on the solution of the optimization problem. It frequently uses few collocation points initially and increases the number of collocation points only as necessary, thereby keeping the number of deterministic PDE solves low while guaranteeing convergence. Currently an error indicator is used to estimate gradient errors due to adaptive stochastic collocation. The algorithm is applied to three examples, and the numerical results demonstrate a significant reduction in the total number of PDE solves required to obtain an optimal solution when compared with a Newton conjugate gradient algorithm applied to a fixed high-fidelity discretization of the optimization problem

Strengthening Community Empowerment Through Village Owned Business Entities In Langkomu Village

Sebagaimana desa-desa pada umumnya, desa Langkomu juga memiliki Badan Usaha Milik Desa (BUMDes) yang didirikan pada tahun 2018 dan diberi nama “Teluk Sejahtera”. BUMDes berperan penting dalam pemerataan pembangunan dan kesejahteraan antara perkotaan dan pedesaan melalui kegiatan pemberdayaan masyarakat desa. Tujuan dari kegiatan ini adalah untuk memberikan pemahaman kepada masyarakat desa Langkomu tentang pentingnya pengelolaan BUMDes yang baik agar tercipta desa mandiri. Kegiatan ini dilaksanakan dalam bentuk seminar, diskusi, dan observasi di desa Langkomu. Kondisi sumber daya manusia dan sumber daya alam yang terletak di pesisir pantai dan adanya teluk beserta barisan pulau-pulau kecil memberikan potensi yang sangat besar bagi desa Langkomu untuk diberdayakan sebagai unit usaha dari BUMDes Teluk Sejahtera. Jenis usaha yang dapat dikembangkan oleh BUMDes adalah kawasan pariwisata, usaha perantara untuk hasil produksi warga desa, usaha produksi ataupun usaha bersama

Integration of Sequential Quadratic Programming and Domain Decomposition Methods for Nonlinear Optimal Control Problems

Summary. We discuss the integration of a sequential quadratic programming (SQP) method with an optimization-level domain decomposition (DD) preconditioner for the solution of the quadratic optimization subproblems. The DD method is an extension of the well-known Neumann-Neumann method to the optimization context and is based on a decomposition of the first order system of optimality conditions. The SQP method uses a trust-region globalization and requires the solution of quadratic subproblems that are known to be convex, hence solving the first order system of optimality conditions associated with these subproblems is equivalent to solving these subproblems. In addition, our SQP method allows the inexact solution of these subproblems and adjusts the level of exactness with which these subproblems are solved based on the progress of the SQP method. The overall method is applied to a boundary control problem governed by a semilinear elliptic equation.