1,720,980 research outputs found
Efficient energy-aware controller placement in software-defined wireless sensor networks
Full text linkMSc (Computer Science), North-West University, Mahikeng CampusA centralized controller in the Software-defined Wireless Sensor Networks (SDWSN)
environment poses a single point of failure and is inapt for a large-scale network. As leverage,
multiple controllers have been introduced but are confronted with controller placement
problems (CPP) for a better quality of service and network requirements. CPP challenge in
SDWSN lies in finding the numbers, location and allocation of controllers in given network
topology as well as sensors assignments. This is important in positively impacting the
network’s performance in terms of latency and cost minimization and reliability, and energy
efficiency maximization. Moreover, several Software-defined networking (SDN) based CPP
approaches have been proposed and developed over the years but only a few proposed
techniques addressed energy efficiency in the SDWSN. Therefore, an efficient and dynamic
CPP approach that is generic and considers energy consumption is important in the SDWSN.
In this research, a hybrid central CPP algorithm is designed and developed to reduce or get rid
of the wireless sensor network (WSN) and SDN-based network performance objectives for
improved SDWSN network performance. The proposed algorithm considered energy
consumption, propagation latency and cost metrics to prolong the lifetime of wireless sensors
and minimize the delay and cost spent for placements of controllers in networks. The algorithm
is associated with real controllers and wireless sensor devices that use certain types of modules.
Furthermore, the technique utilized the threshold-sensitive energy efficient sensor network
(TEEN) routing protocol and particle swarm optimization (PSO)- K-means algorithm chosen
after empirical evaluations were performed with other protocols and algorithms. The approach
is evaluated through a series of simulations and the results indicate that the proposed efficient
CPP energy-aware algorithm is effective on SDWSN in terms of number, location and
allocation of controllers compared to the traditional SDN and WSN. The proposed algorithm
also outperformed other algorithms and significantly increases propagation latency. The
proposed algorithm minimizes delay and improves energy consumption however, it is short on
reliability and load balancing which is part of the future work. We, therefore, recommend using
real or emulated CC2530 devices to create an open-source architecture and framework that can
be used on network simulation tools to test centrally designed algorithms in SDWSN.Master
An Integrated Framework for Controllers Placement and Security in Software-Defined Networks Ecosystem
Full text linkIn the evolving landscape of Software-Defined Networking (SDN), the strategic placement of controllers poses a critical challenge that necessitate a precise balance between network performance and security. This paper presents an integrated framework for enhancing security and performance in SDN by combining controller placement and intrusion detection systems (IDS). Unlike existing solutions which were implemented disjointedly, we propose a holistic approach that leverages the proximity of controllers to network traffic for real-time threat detection, rapid response, and mitigation of security attacks. We employ an advanced clustering model for optimal controller placement, reducing costs and latency while ensuring reliability and balanced loads. In addition, we utilize k-nearest neighbour (KNN) for efficient anomaly detection in our IDS for improved network security. Experimental results confirm the framework’s effectiveness in strengthening SDN security and resilience. The enhanced-DBSCAN-based CPP model significantly minimized the cost, and latency, and ensured continuous operation in dynamic SDN environments while the KNN-based IDS shows effectiveness in improving threat detection capabilities, achieving high detection accuracy of 100% on the LAN dataset, outperforming other machine learning models such as Random Forest and Naïve Bayes. The indication is that strategic controller deployment, in conjunction with IDS, can significantly bolster threat detection, response times, and the overall security stance of the SDN environment
Tool support for LoRaWAN development: a comparative perspective on simulation and emulation
Get PDFThis paper explores the use of various long range wireless area network (LoRaWAN) simulation and emulation tools when designing and evaluating IoT networks. Simulation tools are often popular with researchers because they are less costly and can easily simulate large-scale networks, allowing for easy and faster tests of the scalability of various protocols and behaviors. However, they often lack the unpredictable nature of real deployments. Emulation and cloud-based tools fill this gap, but with their flexibility they provide a more realistic approximation of real-world performance and allow easier interfacing with actual network hardware infrastructure, although they generally incur a higher cost which is often controlled by technical skill level use.
A Balancing Energy Efficiency and Security in CR-LoRaWAN Ecosystems
No full textCognitive Radio-enabled Long Range Wide Area Networks (CR-LoRaWAN) plays an important role in IoT applications. However, due to the limitations of devices and dynamic scheduling mechanisms of the channels, there is still a challenge to balance energy efficiency against security. This paper proposes two developed algorithms that address these challenges: Algo A and Algo B. Algo A ensures key security by mitigating nonce generation vulnerabilities through the replacement of insecure random numbers with prime numbers. Algo B develops this basis by further improving energy efficiency through optimization in session key generation and device management, adding security to it. Both the algorithms incorporate prime numbers in their session key generation that are verified by the Rabin-Miller test and the Sieve of Eratosthenes, with incorporated solar energy harvesting to give a longer life to such devices. Cognitive radio technology is integrated into it for dynamic and intelligent channel selection. Extensive simulations demonstrate that Algo A is much better at handling data with key security, while Algo B outperforms Algo A on energy consumption reduction by 20% and enhancement of overall network security by 15%. These results reveal that Algo B has a better trade-off between security and energy efficiency; hence, Algo B is more suitable for practical deployment. The work further enhances the sustainability and reliability of CR-LoRaWAN networks, especially in resource-constrained environments
A A Comprehensive Review of Energy Optimization Techniques in the Internet of Things
No full textThe advancement of energy efficiency in the Internet of Things (IoT) and wireless sensor networks (WSNs) is an important research effort, given their rapid application expansion across smart cities and homes, healthcare, agriculture, and industrial automation. This paper conducted a comprehensive survey of existing innovative solutions to challenges focusing on hardware-based, software-driven, and network optimization approaches, alongside artificial intelligence-driven and demand-side energy management, and security-enhanced frameworks. 82 peer-reviewed journal articles and conference papers published between 2021 and 2025 were reviewed, using sources such as IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Google Scholar. It identifies significant developments in energy-efficient techniques, including ultra-low-power hardware, adaptive scheduling, bio-inspired clustering, and energy harvesting. Others include intelligent optimization methods(e.g. machine, quantum-inspired heuristics), and blockchain-enhanced security. A structured evaluation process is implemented, following PRISMA guidelines, categorizing studies, and synthesizing findings to highlight technological progress, challenges, and future research directions. The findings show a growing trend towards integrated, multi-objective routing and cross-layer energy optimizations, with significant progress in minimizing energy use, network lifetime and improving security mechanisms. However, challenges like scalability, computational overhead and real-world deployment issues persist. Our study offers valuable insights for sustainable energy management in IoT and WSNs and helps guide future development toward more resilient, adaptable and sustainable energy-aware systems
Enhancing Software Maintenance via Early Prediction of Fault-Prone Object-Oriented Classes
No full text Object-oriented software (OOS) is dominating the software development world today and thus, has to be of high quality and maintainable. However, their recent size and complexity affects the delivering of software products with high quality as well as their maintenance. In the perspective of software maintenance, software change impact analysis (SCIA) is used to avoid performing change in the “dark”. Unfortunately, OOS classes are not without faults and the existing SCIA techniques only predict impact set. The intuition is that, if a class is faulty and change is implemented on it, it will increase the risk of software failure. To balance these, maintenance should incorporate both impact and fault-proneness (FP) predictions. Therefore, this paper propose an extended approach of SCIA that incorporates both activities. The goal is to provide important information that can be used to focus verification and validation efforts on the high risk classes that would probably cause severe failures when changes are made. This will in turn increase maintenance, testing efficiency and preserve software quality. This study constructed a prediction model using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a tool called Class Change Recommender (CCRecommender) was developed to assist software engineers compute the risks associated with making change to any OOS class in the impact set. </jats:p
Fault Tolerance in Mobile Agents : State-of-the-Art and Challenges
No full textThe flexibility offered by mobile agents is quite noticeable in distributed computing environments. But the flexibility comes with a set of new levels of complexity due to their autonomous nature. The mobile agent paradigm introduce additional threats since agents systems are prone to failures originating from bad communication, security attacks, agent server crashes, system resources unavailability, network congestion or even deadlock situations. In such events, mobile agents either get lost or damaged (partially or totally) during execution. In order to gain solid foundation at the heart of today’s e-society, the mobile agent technology paradigm must address the issue of reliability. Making mobile agents fault tolerant is a measure taken to increase the dependability and reliability of agent-based application. Mobile agent’s fault tolerance is gaining momentum and many approaches have been proposed. Despite these efforts, the field still suffers from set backs in the form of persistent challenges. This study analyzes the existing fault tolerance approaches against a proposed generic fault tolerance framework that consists of a monitoring, planning and recovery process execution phases. Following the analysis, this study brings about the state-of-the-art in mobile agent’s fault tolerance approaches and the lingering challenges that affect mobile agent’s fault tolerance implementations from being efficiently and fully realized. The study will serve as a guide to future researches and possible solution for a more reliable and transparent fault tolerance in mobile agents
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Early failure prediction during change impact analysis for improving object-oriented software maintenance
Full text linkPhD (Computer Science), North-West University, Mafikeng Campus, 2014Change is inevitable and an important property of software. Software applications are
changed during their life-time in order to remain useful. Nonetheless, changes also come with
high risks when it is made. Regardless of their size, they can have significant and unexpected
effects elsewhere in the software, degrade software quality or cause them to fail. Change
impact analysis is used to preserve software quality. Today, as object-oriented technology has
gained worldwide popularity, several object-oriented software applications are currently in
use. Given the critical context, it is important that these systems are effectively and efficiently
maintained if continuous usefulness is the goal. However, object-oriented paradigm
introduces specific features, have different change and complex dependencies types often
which makes it hard to identify the impact of changes or it is likely that they might introduce
some types of faults which are difficult to detect. In addition, the available impact analysis
techniques offer litter or no information on explicit program representation, they are not
precise and produce large impact sets which are not good for practical use. Hence, an
effective technique that can precisely predict true impact set and identify early enough, which
components affected by a change are fault-prone is needed. This is necessary to reduce the
risk associated with field failures when changes are made. Moreover, traditional research on
software change impact analysis and fault prediction is disjointed. Therefore, in this research
work, we design a change impact analysis framework that incorporates both impact and early
fault prediction in the maintenance of object-oriented software. The objective is to enhance
program comprehension, reduce the time, effort and the risks associated with software change
while software quality is preserved. We achieved this by exploring and analyzing object-oriented
programs complex relationships, using intermediate source code representation that
explicitly reveals their implicit structure, dependencies and allow for complexity
quantification in small to medium sized systems. The representation alongside the impact
diffusion range of a given change type is used to predict change impact and improve its
precision. Additionally, logistic regression was used to build an early fault prediction model
which utilizes object-oriented product and process metrics. The approaches were empirically
evaluated and the results obtained showed that the source code representation is effective and
practical for impact analysis and the change impact analysis technique showed improved
precision. Also, the fault prediction model shows high accuracy, sensitivity and specificity.
To facilitate the prediction process, this research implemented a novel tool called
ClassChangeRecommender to assist software maintainers in predicting which components
impacted by a change are fault-prone to allow mitigation action in advance before actual
changes are made.Doctora
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