Self-adaptive Software Architecture Research Articles

Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling

With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.

An Optimized Component Selection Algorithm for Self-Adaptive Software Architecture Using the Component Repository

Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.

LiveBox: A Self-Adaptive Forensic-Ready Service for Drones

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly expected to operate in spaces populated by humans while avoiding injury to people or damaging property. However, incidents and accidents can, and increasingly do, happen. Traditional investigations of aircraft incidents require on-board flight data recorders (FDRs); however, these physical FDRs only work if the drone can be recovered. A further complication is that physical FDRs are too heavy to mount on light drones, hence not suitable for forensic digital investigations of drone flights. In this paper, we propose a self-adaptive software architecture, LiveBox, to make drones both forensic-ready and regulation compliant. We studied the feasibility of using distributed technologies for implementing the LiveBox reference architecture. In particular, we found that updates and queries of drone flight data and constraints can be treated as transactions using decentralised ledger technology (DLT), rather than a generic time-series database, to satisfy forensic tamper-proof requirements. However, DLTs such as Ethereum, have limits on throughput (i.e. transactions-per-second), making it harder to achieve regulation-compliance at runtime. To overcome this limitation, we present a self-adaptive reporting algorithm to dynamically reduce the precision of flight data without sacrificing the accuracy of runtime verification. Using a real-life scenario of drone delivery, we show that our proposed algorithm achieves a 46% reduction in bandwidth without losing accuracy in satisfying both tamper-proof and regulation-compliant requirements.

An optimised component selection algorithm for self-adaptive software architecture using the component repository

An optimised component selection algorithm for self-adaptive software architecture using the component repository

Optimal component architecture using particle swarm optimization algorithm for self-adaptive software architecture

The component-based software engineering (CBSE) ensue the procedure of reconfiguration and reusability of components to reap the higher productivity. The context-aware structures are portion of CBSE, which observes the functionality of the system and adopt automatically according to the execution context. In this paper, we are focusing on the aware context guidelines that automatically adapt to the given context given by the customers and remodel the software architecture based totally on the requirements. The component repository turned into added, in which it carries the wide variety of reusable components. The fuzzy logic becomes carried out to the component evaluation in the component repository. The Particle Swarm Optimization (PSO) algorithm applied, to optimize component architecture. The Hospital management system is used to test the adaptability of the system.

An Optimized Component Selection Algorithm for Self-Adaptive Software architecture using the Component Repository

Component-based software engineering focus on the development and reuse of the component. The component reuse is depending on the storage and retrieves process. The storage and retrieve process is carried by a component repository. This paper presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according the functionality and requirements is a crucial part. This paper proposed an algorithm for optimising component selection with the functionality constraints like customer size, reliability and performance. The experimental result evaluates the performance of the algorithm and it is proved that the proposed algorithm had better performance in terms of component selection.

Collective Self-adaptive Software Architecture Specification: Understanding Uncertainty in Cyber-Physical Convergence

There had been past attempts at making adaptation strategies with analytic model that used the current environment information in Cyber-physical System (CPS) to keep the software architecture from deteriorating. However, the research is still in its infancy on the closely- related issue of how to take corrective self-adaptive actions to reconcile the CPS system behavior with the variability of the survival environment. In particular, architects have almost no assistance in reasoning about questions such as: How should we stage the architectural evolution to improve self-adaptation to accommodate uncertainties in CPS? In this work, the specification of software architecture is extended using CHAM (Chemical Abstract Machine) in the presence of uncertainty. The key benefits of our approach are that it leverages standard software architecture models, and quantifies behaviors within the system in terms of relevant architectural elements. Compared to the traditional model, the proposed method could arrange optimized response sequence and adjust the behaviors within the software system to be adaptive to the new situations over time in CPS.

Applying Bigraph Theory to Self-Adaptive Software Architecture

Existing theories of mobile and concurrency calculi for dynamic software architecture can not provide powerful support for evolutionary properties of self-adaptive software.Under this circumstance,Bigraph is based on a graphical model of mobile computation that emphasizes both locality and connectivity and has a complete and extensible theory framework.Therefore,Bigraph can provide a sound concept and intuitive,pervasive expression for self-adaptive software architecture.This paper introduces Bigraphical theory and current research,describes self-adaptive software architecture formally,analyzes and verifies some properties of dynamic evolution,and then discusses some promising directions of formal self-adaptive software architecture.

Applying Bigraph Theory to Self-Adaptive Software Architecture

Applying Bigraph Theory to Self-Adaptive Software Architecture

Study on Environmental Changes Processing in Deep Web Integration Based on Knowledge

Based on the research on the dependence of the components in the deep Web integration (executive partial order and knowledge dependency), a knowledge-based method is given to process the changes in such integration, which includes environmental changes processing model, a self-adaptive software architecture and algorithm. This method can provide a reference to the further research or toward application for the large-scale deep Web integration. The experimental results show that the method can not only process the changes, but also highly improve the performance of the integrated system.