ABSTRACT This work highlights the critical need for highly reliable systems across various fields of science and technology. It emphasises the significance of achieving maximum reliability while operating within constraints such as cost, weight and volume. To address this challenge, the study introduces an innovative approach to enhance the reliability of Cold Standby systems. The proposed method involves incorporating a combination of cold standby components (RRAP‐CM‐CS) with advanced optimisation techniques, specifically utilising a hybrid of particle swarm optimisation and grey wolf optimiser (HPSGWO). The results obtained from simulations and real‐world tests demonstrate a substantial improvement in the reliability of benchmark systems. The approach not only enhances system reliability but also surpasses the performance of traditional methods. This paper provides valuable insights into a practical and effective strategy for strengthening systems by intelligently mixing components and leveraging optimisation strategies.

ABSTRACT A conventional content‐based image retrieval system (CBIR) extracts image features from every pixel of the images, and its depiction of the feature is entirely different from human perception. Additionally, it takes a significant amount of time for retrieval. An optimal combination of appropriate image features is necessary to bridge the semantic gap between user queries and retrieval responses. Furthermore, users should require minimal interactions with the CBIR system to obtain accurate responses. Therefore, the proposed work focuses on extracting highly relevant feature information from a set of images in various natural image databases. Subsequently, a feature‐based learning/classification model is introduced before similarity measure calculations, aiming to minimise retrieval time and the number of comparisons. The proposed work analyses the learning models based on the retrieval system's performance separately for the following features: (i) dominant colour, (ii) multi‐resolution radial difference texture patterns, and a combination of both. The developed work is assessed with other techniques, and the results are reported. The results demonstrate that the implemented ensemble learning model‐based CBIR outperforms the recent CBIR techniques.

Abstract This article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.

Abstract In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed.

Abstract Cephalometric analysis has an important and essential role to treat the patients with craniofacial and dentofacial deformities. Cephalometric analysis is a relationship of human geometry which can be quantified and derived from the linear and angular measurements. To treat any patient, such analysis is required to be performed on the Head X‐ray image of the patient. The objective of the proposed work is to detect cephalometric landmarks automatically on CT (computational tomography) images. Twenty cephalometric landmarks were automatically localized on 100 CT scans using hybrid coarse‐to‐fine computational method. The mean error for landmark detection was computed as 2.88 mm and standard deviation of 1.85 mm. The highest detection rate for cephalometric landmarks was received as 100% for Nasion landmark under 4‐mm error and the highest detection rate was received as 99% for Nasion landmark under 3‐mm error. The less number of datasets were used for the training and higher number of datasets were used for the testing. Compared to the literature methods, our method used higher number of datasets to demonstrate the accuracy of the proposed method.

The evolution of Internet-of-Vehicles (IoV) from IoT has revolutionized Smart cities, enabling vehicle communication for safety and traffic information dissemination. However, fulfilling time-sensitive applications like safety alerts via Cellular Vehicle-to-Everything (C-V2X) faces resource constraints. This study presents a Non-Orthogonal Multiple Access (NOMA) based resource allocation for C-V2X in Ultra-dense networks (UDN). This paper has also discussed the role of TinyML in unmanned aerial vehicle (UAV) and it is demonstrated with use case scenario. Additionally, a generalized expression for scheduling time fraction is derived for the proposed scheme. The proposed framework optimizes power allocation, accommodating high-speed users and UAV scenarios to improve performance in obstructed regions. Numerical analysis demonstrates an approximate 85% throughput increase over conventional schemes, affirming the efficiency of the NOMA-based approach for enhanced C-V2X performance.

The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Abstract The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers.

Abstract Agriculture in rural areas facing critical issues such as irrigation with the increase in water crises followed by some other issues line seed quality, poor fertilizers and many others. The recent advances suggest that IoT and Blockchain Technology along with artificial intelligence will be most dominant technologies in near future. In this article, the integration of Internet of Things (IoT) with Blockchain technology is implemented for monitoring agricultural fields efficiently. An efficient seed quality monitoring and smart water management system is design using IoT and Blockchain Technology for managing and coordinating the use of good quality seeds and water resources among communities. The Blockchain network is implemented for securing the information and supporting trust among the members of community. The Blockchain network is also implemented for sporting trust among commercial resource constrained systems, which are communicating with the Blockchain network consisting of a hardware platform. The design of a prototype and its performance evaluation based on implementation is also presented.

Presently, trillions of Internet of Things (IoT) devices are in use, with many more projected to join IoT networks in the future. These IoT devices create a massive volume of data, which cannot be transmitted over the network without proper security and privacy. Furthermore, as the amount of information and variety of interconnected devices grows, problems, including excessive response time, bandwidth constraints, and scalability, emerge in proper network design. To solve the constraints of today’s smart cities for next-generation networks, an effective, secure, and scalable distributed framework must be designed bringing computing and storage resources nearer to endpoints. In this article, combining the strengths of software-defined networks (SDNs) and blockchain technology, an innovative adaptable network infrastructure for smart cities is developed. The network is divided into different domains in which SDN will detect potential attacks and transmit the secured data to the blockchain. Our in-depth experimental analysis on performance evaluation show that the proposed framework achieves 12.75% improvement over baseline methodologies.

Blockchain technology (BCT) has brought a paradigm shift to Business Process Management (BPM). BCT provides a trusted decentralized infrastructure to secure data and process executions using distributed ledgers and smart contract to manage complex business processes. Numerous efforts have been made to exploit BCT in supporting dynamic and trusted collaborations of business processes. This paper aims to understand recent BCT development for its BPM applications and identify the limitations and challenges for further development via a systematic literature review (SLR). It is found that numerous works have reported using BCT as technical solutions to fulfill some traditional BPM functions. This paper is distinguished from existing works, especially several relevant surveys in the sense that (1) the impact of using BCT in BPM is thoroughly explored to identify new constraints and challenges explicitly brought by blockchains; (2) the requirements for Business Process Compliance (BPC) are firstly analyzed in detail. Note that BPC is to assure the adherence of business processes to pre-defined policies, standards, specifications, regulations, and laws when business processes are executed. To fill the gaps of BCT applications in these two aspects, Blockchain-as-a-Service (BCaaS) is adopted in business process architecture, and the trends of BCT developments are identified accordingly.

Workflow is used to support and connect business processes (BP) in organizations. Historically, it is used to define the control of how tasks are coordinated and executed. Its importance has been continuously increasing with the incorporation of rapidly developed Service-oriented Architecture (SoA), Blockchain, and Internet-of-Thing (IoT); new information technologies have expanded the coverage of workflow across various applications. Workflows often interact with services, where SoA is a key driver to smoot service discovery and provide standards for interoperation. In decentralized collaborative environments, a workflow often deals with disparate services dynamically and interacts with services on demand. This not only unlocks the potentials of workflow applications in business process managements (BPM), but also precipitates significant challenges and has brought considerable attentions to the research community. This paper investigates the state-of-the-art of service workflow modelling and enabling technologies. It begins with the identification and examination of workflow and IoT characteristics; it proposes a workflow architecture to classify existing works on service workflows, and it summarizes the methods for workflow modeling, service interoperation to identify the limitations of existing works and clarify future research directions in using service workflows.

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

The industrial internet of things (IIoT) and Industry 4.0/5.0 enable the integration of machinery, equipment, processes, and humans across a variety of vertical sectors, including manufacturing and logistics supply chains, transportation, and medical care (Yadav et al., 2022). Several types of sensor nodes link these interconnected machines/appliances, sensing and transmitting data to the nodes or the cloud. To manage those links, next-generation networking technologies, such as 6G and Cybertwin, are being introduced. Sixth-generation (6G) communication will be critical in providing complex wireless interconnections, with 6G networks expected to be capable of supporting millions of linked devices and systems while maintaining high data rates and low latency (Kanwal et al., 2022). Blockchain is one emerging technology that can contribute to IIoT stability. Blockchain looks to offer a solution to maintain user privacy while still preserving the capacity for immutable information and replication. A blockchain technology is used to enforce a free distributed ledger to record transactions by peer-to-peer network nodes (P2Ps) and to avoid the need for a central authority through a distributed consensus process. This special issue focuses on six high-quality studies that address real-world issues in IIoT. Sharma et al. (2021) suggested a blockchain-based IoT architecture that uses the identity-based encryption (IBE) algorithm to improve the security of healthcare data. In this case, the smart contract outlines all of the fundamental processes of the healthcare system, which can benefit all stakeholders. Many tests are carried out in order to assess the efficacy of the suggested strategy. The findings reveal that the suggested system outperforms the current well-known strategies. Al-Haija et al. (2022) built a strong classifier for identifying and categorizing various cyberattacks in IoT networks using the AdaBoost machine learning algorithm paired with decision trees and substantial data engineering techniques. We test our system using the TON IoT 2020 datasets, which are a collection of datasets designed particularly for three-layered IoT systems that include physical, network, and application layers. We compare our system's performance to that of existing cutting-edge technologies. Our experimental results show that our framework is capable of offering improved classification accuracy and reduces kinds 1 and 2 mistakes for building more durable IoT infrastructures. Babu et al. (2022) introduced a unique IoT device authentication technique based on IBE and a blockchain network. Blockchain is utilized as a distributed PKG, removing the single point of failure and key escrow issue associated with PKGs. Furthermore, the suggested work is implemented on Hyperledger Fabric, an open-source blockchain platform that effectively handles the adding, updating, and deletion operations required for successful IoT device authentication and communication. Mehbodniya et al. (2022) created a framework for signature creation and verification using a modified Lamport Merkle Digital Signature technique. It employs a central healthcare controller (CHC) to determine the origin of the created signature as well as verification and authentication. To validate the signature, the validation hash public key with create key is necessary. When compared with conventional approaches, this resulted in more efficient, cost-effective, and speedier security. Vigneysh et al. (2022) provided a successful technique for increasing dynamic responsiveness during system uncertainties such as voltage distortions, frequency changes, renewable energy source variations, and the presence of non-linear and unbalanced loads. It also increases the quality of current fed into the grid during uncertainty. The suggested control approach is used to manage both the dc side capacitor voltage and the current loop of a grid-connected inverter. The suggested system is simulated in the MATLAB Simulink environment, and its performance is compared with that of standard controllers to demonstrate the efficacy of the proposed control technique during system anomalies. Patil et al. (2022) developed a two-phased technique for blockchain and IoT federated networks that use a multi-criteria-based approach to connection selection. The dynamic gateway scheduling technique is capable of supporting both blockchain-based transactions and IoT device connectivity. Furthermore, the suggested technique improves the fairness of data transfer for each gateway, resulting in efficient data transmission. Before using the link selection process, machine learning (ML) approaches are used to examine the state of the communication channels. The links are then selected using multi-criteria statistical approaches. Finally, scheduling is carried out in order to identify the best gateway for quickly channelling blockchain data.

Abstract The emergence of smart cities and sustainable development has become a globally accepted form of urbanization. The epitome of smart city development has become possible due to the latest innovative integration of information and communication technology. Citizens of smart cities can enjoy the benefits of a smart living environment, ubiquitous connectivity, seamless access to services, intelligent decision making through smart governance, and optimized resource management. The widespread acceptance of smart cities has raised data security issues, authentication, unauthorized access, device‐level vulnerability, and sustainability. This article focuses on the holistic overview and conceptual development of smart city. Initially, the work discusses the smart city idea and fundamentals explored in various pieces of literature. Further various smart city applications along with notable implementations, are put forth to understand the quality of living standards. Finally, the article depicts a solid understanding of different security and privacy issues, including some crucial future research directions.

Abstract The internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Abstract With new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT.

Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost-efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range-free approaches, distance vector-Hop (DV-Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far-off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV-Hop. In the most recent approach, meta-heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV-Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p-value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty-eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.

The mobile traffic data and resources using IoE in wireless networking have raised numerous problems in terms of performance monitoring in edge-connected devices [1]. Next-generation networks, such as 6G and cybertwin, are implemented to address these problems. Sixth-generation (6G) communication would play a vital role in supporting complex wireless interconnectivity. In order to allow millions of connected devices and applications to operate smoothly at high data rates and low latency, a network of the 6G is anticipated [2]. The only access point for the Internet is cybertwin, which serves as a contact hub and tracks all user requirements. In the edge-cloud cyberspace, cybertwin is a digital database of smartphone activities, terminals, objects, etc. The integrated use of technology such as blockchain, 6G, and cybertwin is a multidisciplinary area for designing effective and efficient IoE systems [3]. The purpose of this Special Issue is to examine the new technology, innovative architectures, and future problems in depth in terms of network secured infrastructure based on cybertwin for 6G-enabled IoE.

Internet of Things (IoT) is a phenomenon involving connecting things or objects with sensors. The IoT market is growing rapidly, and there are strong incentives for companies to follow the trend of IoT growth and development. However, the percentage of IoT measures that are considered successful seems low. The complexity of carrying out an IoT project lies in the need to adjust all the pieces of the puzzle: assets, sensors, communications, technology, coverage, and geographical locations with precision of the measures and regulations. All these requirements determine the economic viability of the business and its benefit. This study, therefore, examines how the project methodology can support the development of the concept and ensure the business value of IoT initiatives. The project methodology developed in this study is called PoC Design. A case study was evaluated, in which defects in street lighting were investigated and carried out. The evaluation of the methodology highlighted the importance of defining problems and solutions based on business value, calculating the potential of an IoT initiative, determining the continuation of the project, involving stakeholders at an early stage, and creating a PoC to validate the concept with stakeholders.

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short‐term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

Recently, there have been exploratory growth in the research of wireless sensor network due to wide applications like health monitoring, environment monitoring, and urban traffic management. Sensor network applications have been used in habitat monitoring, border monitoring, health care, and military surveillance. In some applications, the security of these networks is very essential and need robust support. For a network, it is very important that node in the network trust each other and malicious node should be discarded. Cryptography techniques are normally used to secure the networks. Key plays a very important role in network security. Other aspects of security such as integrity, authentication, and confidentiality also depend on keys. In wireless sensor network, it is very difficult to manage the keys as this includes distribution of key, generation of new session key as per requirements, and renewal or revoke the keys in case of attacks. In this paper, we proposed a scalable and storage efficient key management scheme (SSEKMS) for wireless sensor networks that establish the three types of keys for the network: a network key that is shared by all the nodes in the network, a cluster key shared for a cluster, and pairwise key for each pair of nodes. We analysed the resiliency of the scheme (that is the probability of key compromise against the node capture) and compared it with other existing schemes. SSEKMS is a dynamic key management system that also supports the inclusion of the new node and refreshes the keys as per requirements.

Clinical decision support systems (CDSSs) assist clinicians with patient diagnosis and treatment. However, inadequate attention has been paid to the process of selecting and buying systems. The diversity of CDSSs, coupled with research obstacles, marketplace limitations, and legal impediments, has thwarted comparative outcome studies and reduced the availability of reliable information and advice for purchasers. We review these limitations and recommend several comparative studies, which were conducted in phases; studies conducted in phases and focused on limited outcomes of safety, efficacy, and implementation in varied clinical settings. Additionally, we recommend the increased availability of guidance tools to assist purchasers with evidence-based purchases. Transparency is necessary in purchasers' reporting of system defects and vendors' disclosure of marketing conflicts of interest to support methodologically sound studies. Taken together, these measures can foster the evolution of evidence-based tools that, in turn, will enable and empower system purchasers to make wise choices and improve the care of patients.

In this paper we present a system for online power prediction in virtualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error of less than 10%, outperforming state of the art regression based approaches at negligible runtime overhead.

Runtime characteristics of individual threads (such as IPC, cache usage, etc.) are a critical factor in making efficient scheduling decisions in modern chip-multiprocessor systems. They provide key insights into how threads interact when they share processor resources, and affect the overall system power and performance efficiency. In this paper, we propose and implement mechanisms and policies for a commercial OS scheduler and load balancer which incorporates thread characteristics, and show that it results in improvements of up to 30% in performance per watt.

In this article, we present vGreen, a multitiered software system for energy-efficient virtual machine management in a clustered virtualized environment. The system leverages the use of novel hierarchical metrics that work across the different abstractions in a virtualized environment to capture power and performance characteristics of both the virtual and physical machines. These characteristics are then used to implement policies for scheduling and power management of virtual machines across the cluster. We show through real implementation of the system on a state-of-the-art testbed of server machines that vGreen improves both average performance and system-level energy savings by close to 40% across benchmarks with varying characteristics.

In this paper, we propose PDRAM, a novel energy efficient main memory architecture based on phase change random access memory (PRAM) and DRAM. The paper explores the challenges involved in incorporating PRAM into the main memory hierarchy of computing systems, and proposes a low overhead hybrid hardware-software solution for managing it. Our experimental results indicate that our solution is able to achieve average energy savings of 30% at negligible overhead over conventional memory architectures.

In this paper, we present vGreen, a multi-tiered software system for energy efficient computing in virtualized environments. It comprises of novel hierarchical metrics that capture power and performance characteristics of virtual and physical machines, and policies, which use it for energy efficient virtual machine scheduling across the whole deployment. We show through real life implementation on a state of the art testbed of server machines that vGreen can improve both performance and system level energy savings by 20% and 15% across benchmarks with varying characteristics.

In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve them using our algorithm. The selection is done among a set of experts, which refers to a set of DPM policies and voltage-frequency settings, leveraging the fact that different experts outperform each other under different workloads and device leakage characteristics. The online-learning algorithm adapts to changes in the characteristics and guarantees fast convergence to the best-performing expert. In our evaluation, we perform experiments on a hard disk drive (HDD) and Intel PXA27x core (CPU) with real-life workloads. Our results show that our algorithm adapts really well and achieves an overall performance comparable to the best-performing expert at any point in time, with energy savings as high as 61% and 49% for HDD and CPU, respectively. Moreover, it is extremely lightweight and has negligible overhead.

This paper presents an extremely lightweight dynamic voltage and frequency scaling technique targeted towards modern multi-tasking systems. The technique utilizes processors runtime statistics and an online learning algorithm to estimate the best suited voltage and frequency setting at any given point in time. We implemented the proposed technique in Linux 2.6.9 running on an Intel PXA27x platform and performed experiments in both single and multi-task environments. Our measurements show that we can achieve the maximum energy savings of 49% and reduce the implementation overhead by a factor of 2 when compared to state of the art techniques.

Dynamic power management (DPM) work proposed to date places inactive components into low power states using a single DPM policy. In contrast, we instead dynamically select among a set of DPM policies with a machine learning algorithm. We leverage the fact that different policies outperform each other under different workloads and devices. Our algorithm adapts to changes in workloads and guarantees quick convergence to the best performing policy for each workload. We performed experiments with a policy set representing state of the art DPM policies on a hard disk drive and a WLAN card. Our results show that our algorithm adapts really well with changing device and workload characteristics and achieves an overall performance comparable to the best performing policy at any point of time.