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The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different problems and deliver new services. For example, IoT is the key enabling technology for smart homes. Smart home technology provides many facilities to users like temperature monitoring, smoke detection, automatic light control, smart locks, etc. However, it also opens the door to new set of security and privacy issues, for example, the private data of users can be accessed by taking control over surveillance devices or activating false fire alarms, etc. These challenges make smart homes feeble to various types of security attacks and people are reluctant to adopt this technology due to the security issues. In this survey paper, we throw light on IoT, how IoT is growing, objects and their specifications, the layered structure of the IoT environment, and various security challenges for each layer that occur in the smart home. This paper not only presents the challenges and issues that emerge in IoT-based smart homes but also presents some solutions that would help to overcome these security challenges.

Arithmetic logic unit (ALU), a core component of a processor, is one of the thrust areas of the current research. Presently, ALU is designed by transistor-based CMOS technique and its individual components are placed in different layers. The current design is affected by the limitations of Moore’s law and design complexity. At present, ‘Quantum cellular automata electro-spin (QCA-ES)’ technology is widely accepted technology as an alternative of ‘CMOS’ to minimize the above discussed problems. In this research paper, the design of a novel multilayer portable, dynamic, fault-tolerant, power-efficient, thermally stable reversible ALU is proposed which is explored through QCA-ES. All the arithmetic and logical components of ALU are separately placed in different layers. Area density, delay, fault tolerance and thermal stability are investigated. A specific type of gate, known as reversible gate (modified 3:3 ‘TSG’ gate), is used in this proposed design with QCA technology to get the optimized design ALU with low occupied area, complexity, delay and power dissipation. Investigation of a fault-free design and saturated amplitude level (of output) change with respect to temperature increment in the proposed device are also discussed in this paper. Not only the thermal stability (up to 6 k temperature) but also an investigation on cell complexity of the 100% fault-free (against multiple cell-omission, cell-displacement, cell-orientation change and extra cell deposition), multilayer nano-device is represented in this work. ‘QCA-Designer’ software is used in this research work to design and develop layout of the proposed components in quantum-field and find out the occupied area, delay and complexity of proposed design. ‘QCA-Pro’ software is used for getting the value of dissipated power.

With the development for the technology of mobile edge computing (MEC) and the grave situation for the shortage of global energy, the problem of computation offloading in a cloud computing framework is getting more attention by network managers. In order to improve the experience quality of users and increase the energy efficiency of the system, we focus on the issue of task offloading strategy in MEC system. In this paper, we propose a task offloading strategy in the MEC system with a heterogeneous edge. By considering the execution and transmission of tasks under the task offloading strategy, we present an architecture for the MEC system. We establish a system model composed of M/M/1, M/M/c and M/M/ $$\infty$$ queues to capture the execution process of tasks in local mobile device (MD), MEC server and remote cloud servers, respectively. Moreover, by trading off the average delay of tasks, the energy consumption level of the MD and the offloading expend of the system, we construct a cost function for serving one task and formulate a joint optimization problem for the task offloading strategy accordingly. Furthermore, under the constraints of steady state and proportion scope, we use the Lagrangian function and the corresponding Karush–Kuhn–Tucker (KKT) condition to obtain the optimal task offloading strategy with the minimum system cost. Finally, we carry out numerical experiments on the MEC system to investigate the influence of system parameters on the task offloading strategy and to obtain the optimal results. The experiment results show that the task offloading strategy proposed in this paper can balance the average delay, the energy consumption level and the offloading expend with the optimal allocation ratio.

Efficient resource demand prediction and management are two main challenges for cloud service providers in order to control dynamic autoscaling and power consumption in recent years. The behavior of cloud workload time-series at subminute scale is highly chaotic and volatile; therefore, traditional machine learning-based time-series analysis approaches fail to obtain accurate predictions. In recent years, deep learning-based schemes are suggested to predict highly nonlinear cloud workloads, but sometimes they fail to obtain excellent prediction results. Hence, demands for more accurate prediction algorithm exist. In this paper, we address this issue by proposing a hybrid E2LG algorithm, which decomposes the cloud workload time-series into its constituent components in different frequency bands using empirical mode decomposition method which reduces the complexity and nonlinearity of prediction model in each frequency band. Also, a new state-of-the-art ensemble GAN/LSTM deep learning architecture is proposed to predict each sub band workload time-series individually, based on its degree of complexity and volatility. Our novel ensemble GAN/LSTM architecture, which employs stacked LSTM blocks as its generator and 1D ConvNets as discriminator, can exploit the long-term nonlinear dependencies of cloud workload time-series effectively specially in high-frequency, noise-like components. By validating our approach using extensive set of experiments with standard real cloud workload traces, we confirm that E2LG provides significant improvements in cloud workload prediction accuracy with respect to the mean absolute and standard deviation of the prediction error and outperforming traditional and state-of-the-art deep learning approaches. It improves the prediction accuracy at least 5% and 12% in average compared to the main contemporary approaches in recent papers such as hybrid methods which employs CNN, LSTM or SVR.

Due to the problem of high link load of edge cache and small storage space of edge server, a caching architecture by the collaborative of edge nodes and the cloud server is proposed. The content cache location is designed and optimized, which can be the content provider, cloud server (CS), and edge node (EN). In the proposed system, cloud servers collaborate with edge servers and the performance of content caching can be improved by coordinating caching on the cloud server or caching on the edge server. In this paper, a cloud-edge collaborative caching model based on the greedy algorithm is proposed, which includes the content caching model and collaborative caching model. Network architecture, file popularity estimation, link capacity, and other factors are considered in the model. Correspondingly, a cloud-edge collaborative cache algorithm based on a greedy algorithm is proposed. The related optimization problem is decomposed into the knapsack problem of cache layout in each layer, and then the greedy algorithm is used to solve the knapsack problem of cache placement and cooperative cache proposed in this paper. The affiliation between CS cache and EN caches in the layered architecture is improved and recognized. In the experimental results, the link load is reduced, the cache hit rate is improved by using the proposed method of edge caching, and it also has obvious advantages in the average end-to-end service delay.

With the advent of the Internet of Things (IoT) and ubiquitous presence of sensor nodes, positioning technologies have become a topic of interest among researchers. While the applications of positioning systems are very vast, determining the position of moving sensor nodes, finding missing people in large areas, and object tracking are among the most popular ones. The focus of this paper is to propose a positioning system to locate missing people in theme parks. Currently, radio frequency identification (RFID) systems are used in modern theme parks to locate lost visitors. In these systems, a wristband with active RFID tag is given to each visitor and RFID readers are deployed in predetermined locations. When a visitor is in the communication range of a reader, its location can be estimated based on the location of the reader. Therefore, the accuracy of these systems is relevant to the communication range of readers. Another limitation of RFID-based systems is due to the fact that readers cannot be placed in communication range of each other as they can interference with each other. It is clear that the only way to increase the accuracy of such systems is by increasing the number of readers and decreasing the communication range of each reader. In this paper, a Bluetooth low energy (BLE)-based system is proposed to be used in theme parks for locating lost visitors. The advantage of using BLE is due to the fact that it uses frequency hopping spread spectrum (FHSS) thus readers can be placed in communication range of each other without severe interference. In the proposed method, at first, the optimal places for deploying readers are obtained using ant colony optimization (ACO). Then, a fuzzy approach is used to increase the accuracy of the system. Three different signal levels are defined to be used in our fuzzy system based on which the location of visitors can be estimated. By using three levels of signal strength, the accuracy of the system is increased compared with the similar system with the similar number of readers. The simulation results show that the accuracy of the system is improved using this method, and the cost of the system is decreased as BLE readers are much less expensive than their RFID counterparts.

Wireless sensor networks usually adopt a tree structure for routing, where each node sends and forwards messages to its parent. However, lifetime maximization with tree routing structure is NP-hard, and all algorithms attempting to find the optimal solution run in exponential time unless $$P=\mathrm{NP}$$ . This paper revisits the problem of non-tree routing structure, where a node can send different messages to different neighbors. Though lifetime maximization with non-tree routing can be solved in polynomial time, the existing method transforms it into a series of maximum flow problems, which are either complicated or with high running time. This paper proposes an algorithm with O(mn) running time, where m is the number of edges and n is the number of nodes. The heart of the algorithm is a method to find a routing path from any node to the sink in O(m) time without disconnecting existing routing paths. The proposed algorithm is also suitable for distributed implementation. When a node fails, each influenced node can establish a new routing path in O(m) time. Simulations are conducted to compare the optimal lifetimes of tree structure and non-tree structure on random networks. The results verify the effectiveness of the proposed algorithm.

This paper introduces the application of the induction motor (IM) drive brain emotional intelligent controller (BEIC). Intelligent regulation, modelled on the human brain, is capable of generating impulses and is used as a controller. A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm. This paper proposes that speed estimation using BEIC for direct torque control (DTC) of IM drive. The experimental work is conducted on a hardware-in-loop mechanism using a real-time digital simulator (Op-RTDS-OP5600). The simulation and test results are discussed. The proposed method is compared to the DTC-SVM-based IM drive speed control with the existing controllers.

Recently, emerging edge computing technology has been proposed as a new paradigm that compensates for the disadvantages of the current cloud computing. In particular, edge computing is used for service applications with low latency while using local data. For this emerging technology, a neural network approach is required to run large-scale machine learning on edge servers. In this paper, we propose a pod allocation method by adding various graphics processing unit (GPU) resources to increase the efficiency of a Kubernetes-based edge server configuration using a GPU-based embedded board and a TensorFlow-based neural network service application. As a result of experiments performed on the proposed edge server, the following are inferred: 1) The bandwidth, according to the time and data size, changes in local (20.4–42.4 Mbps) and Internet environments (6.31–25.5 Mbps) for service applications. 2) When two neural network applications are run on an edge server consisted with Xavier, TX2 and Nano, the network times of the object detection application are from 112.2 ms (Xavier) to 515.8 ms (Nano); the network times of the driver profiling application are from 321.8 ms (Xavier) to 495.7 ms (Nano). 3) The proposed pod allocation method demonstrates better performance than the default pod allocation method. We observe that the number of allocatable pods on three worker nodes increases from five to seven, and compared to other papers, the proposed offloading shows similar or better response times in environments where multiple deep learning applications are implemented.

Quantum dot cellular automaton (QCA) is a novel emerging nanometer-scale-based circuit design using nanocomputing technology, which overcomes the limitations of complementary MOS technology in the precondition of the circuit design area, power, and latency/delay. This paper presents an efficient design of crossover single-layer (coplanar) and multilayer novel hybrid full adder–subtractor circuits by implementing majority gate minimization functional J-map technique. The proposed circuits have been found more efficient in terms of minimum number of QCA cells, low latency, required area in µm2, and reduced quantum cost as compared to existing QCA adder–subtractor designs and also avoid the thermodynamics problems occurring due to long QCA wires with the applied synchronization clocking method. In this paper, we have introduced 14 nm × 14 nm and 16 nm × 16 nm cell size QCA circuits and compared with an existing and proposed novel 18 nm × 18 nm single-layer and multilayer designs. Both designs are implemented by the QCADesigner-E tool with bistable vector and coherent vector energy setup in the Euler method and the Runge–Kutta method.

Appearances of specific transition patterns during data transfer in bus lines of modern high-performance computing systems, such as communicating structures of accelerators for deep convolutional neural networks, commercial Network on Chips, and memories, can lead to crosstalk faults. With the shrinkage of technology size, crosstalk faults occurrence boosts and leads to degradation of reliability and performance, as well as the increasing power consumption of lines. One effective way to alleviate crosstalk faults is to avoid the appearance of these specific transition patterns by using numerical-based crosstalk avoidance codes (CACs). However, a serious problem with numerical-based CACs is their overheads in terms of required additional bus lines for representing code words. To solve this problem, in this paper we present a novel CAC that is based on the use of three symbols (three-value) to represent the code words in the bus lines, rather than classical binary CACs based on binary, i.e., 0 and 1 symbols. Our proposed CAC, named summation-based tri-value crosstalk avoidance code (ST-CAC), reduces the worst-case delay in bus lines with respect to binary CACs, and it can efficiently be applied to any arbitrary channel width of lines. The use of three symbols to represent code words in ST-CAC enables to increase the number of code words of a numerical system without increasing the number of required bus lines significantly. The experimental results show that CACs based on the use of three symbols can reduce the number of additional lines compared to binary CACs by 33%. Moreover, we show in the paper, that the delay of wires in the presence our ST-CAC can reduce by 33% with respect to state-of-the-art binary value CACs.

Due to the rapid development of the high-speed wired and wireless Internet, image contents including objects with exposed personal information are being distributed freely, which is a social problem. In this paper, we introduce a method of robustly detecting a target object with facial region exposed from an image that is quickly entered using skin color and a deep learning algorithm and effectively covering the detected target object through prediction. The proposed method in this paper accurately detects the target object containing facial region exposed from the image entered by applying an image adaptive skin color model and a CNN-based deep learning algorithm. Subsequently, the location prediction algorithm is used to quickly track the detected object. A mosaic is overlapped over the target object area to effectively protect the object area where the facial region is exposed. The experimental results show that the proposed approach accurately detects the target object including the facial region exposed from the continuously entered video, and efficiently covers the detected object through mosaic processing while quickly tracking it using a prediction-based tracking algorithm. The tracking-based target covering method proposed in this study is expected to be useful in various practical applications related to pattern recognition and image security, such as content-based image retrieval, real-time surveillance, human–computer interaction, and face detection.

The question answer (QA) system for a reading comprehension task tries to answer the question by retrieving the needed phrase from the given content. Precise answering is the key role of a QA system. An ambiguity is developed when we need to answer a negative question with a positive reply. The negation words change the polarity of the sentence, and hence, the scope of negation words is notable. This has paved the way for studying the role of ‘negation’ in the natural language processing (NLP) task. The handling of these words is considered a major part of our proposed methodology. In this paper, we propose an algorithm to retrieve and replace the negation words present in the content and query. A comparative study is done for performing word embedding over these words using various state-of-the-art methods. In earlier works when handling the negation the semantics of the sentences are changed. Hence, in this paper we try to maintain the semantics through our proposed methodology. The updated content is embedded into the bi-directional long short-term memory (Bi-LSTM) and thus makes the retrieving of an answer for a question answer system easier. The proposed work has been carried over the Stanford Negation, and the SQuAD dataset with a higher precision value of 96.2% has been achieved in retrieving the answers that are given in the dataset.

The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.

With the continual development of deep learning, the image processing in Internet of Things is the key technology. Nevertheless, many deep learning methods cannot deal with the special needs of Internet of Things, for example, the Internet of vehicles and ships for the traffic haze image. Particularly, haze removal in the water area, because of the influence of water vapor, is more difficult than that in the ordinary scene. And the dehazing of water area has practical value in shipping and aerial photography. Sensible dehazing effect can even ensure the safety of navigation. In this paper, a compensation textures dehazing method is presented for water alike scene. The motivation of this paper comes from the following observations. Dark channel haze removal method has a very real dehazing effect for ordinary scenes. However, due to the principle of the dark channel method, this dehazing method has a large deviation in the water alike area. Therefore, based on the classical dark channel method, this paper proposes three innovations. First, a dynamic priority method is designed. This method can calculate the priority order of patches according to the characteristics of the processed subject. Second, a compensation textures method is designed, which can compensate the special area according to the proposed priority method. Third, a new haze removal method is designed, which can effectively remove the haze of water area according to the proposed compensation textures method. The results of visual and quality experiment show that proposed method has a state-of-the-art dehazing result in the water alike area.

Children with autism spectrum disorders (ASDs) have some disturbance activities. Usually, they cannot speak fluently. Instead, they use gestures and pointing words to make a relationship. Hence, understanding their needs is one of the most challenging tasks for caregivers, but early diagnosis of the disease can make it much easier. The lack of verbal and nonverbal communications can be eliminated by assistive technologies and the Internet of Things (IoT). The IoT-based systems help to diagnose and improve the patients’ lives through applying Deep Learning (DL) and Machine Learning (ML) algorithms. This paper provides a systematic review of the ASD approaches in the context of IoT devices. The main goal of this review is to recognize significant research trends in the field of IoT-based healthcare. Also, a technical taxonomy is presented to classify the existing papers on the ASD methods and algorithms. A statistical and functional analysis of reviewed ASD approaches is provided based on evaluation metrics such as accuracy and sensitivity.

In this paper, we focus on various compression and classification algorithms for three different paralinguistic signal classification tasks. These tasks are quite difficult for humans because the sound information from such signals is difficult to distinguish. Therefore, when machine learning techniques are applied to analyze paralinguistic signals, several different aspects of speech-related information, such as prosody, energy, and cepstral information, are usually considered for feature extraction. However, when the size of the training corpus is not sufficiently large, it is extremely difficult to directly apply machine learning to classify such signals due to their high feature dimensions; this problem is also known as the curse of dimensionality. This paper proposes to address this limitation by means of feature compression. First, we present experimental results obtained by using various compression algorithms to compress signals to eliminate redundancy of the signal features. We observe that compared with the original features, the compressed signal features still provide a comparable ability to distinguish the signals, especially when using a fully connected neural network classifier. Second, we calculate the output distribution of the F1-score for each emotion in the speech emotion recognition problem and show that the fully connected neural network classifier performs more stably than other classical methods.

Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise with the help of scheduling algorithms to their respective processors. For task assignment, every scheduling algorithm follows different protocols like upper bound of CPU utilization, assigning priorities, etc. In this paper, author has worked on such scheduling algorithms. Previously, the author evaluated the performance of algorithms on the basis of transactions (group of tasks). In this paper, the author re-evaluates joint EDF–RM scheduling algorithm, where its performance is calculated on the execution of individual task basis. For comparative analysis, similar algorithms are considered, i.e., joint EDF–RMS, earliest deadline first (EDF) and rate monotonic scheduling (RMS). These mentioned algorithms are simulated and analyzed with the help of statistical analysis, and turnaround time of periodic tasks is evaluated. Additionally, migration distribution and CPU utilization on the basis of scheduling algorithms' upper bounds are also calculated.

Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care is the high energy consumption when sending information of sensors to the sink. Choosing the proper route when sending information to the sink will reduce energy consumption and increase network lifetime. The current paper used the shuffled frog leaping algorithm (SFLA) to find an appropriate route that can send information of sensors to the lowest energy to the sink or coordinator. Also, to prevent data traffic in the sink, the sensors are divided into two groups of four. With the advent of the Internet of things and increasing use of them among people, the use of this technology has attracted the attention of remote medical care. Smartphones and smartwatches can measure information from different parts of the body, such as the heart, stroll, and glucose, based on their built-in sensors such as accelerometers, gyroscopes, and advanced cameras. These two smart devices are used in three different roles (coordinator, sink, and sensor) to improve energy efficiency in remote medical care. The paper is shown an appropriate path to reduce energy consumption in sensors using a smartphone, smartwatch, and the use of SFLA algorithms (REC-SSS). The simulation results show that the network stability increased by 12.5%,132%, and 3.5% compared to SIMPLE, M-ATTEMPT, and EERP, respectively. Also, in the proposed schema, the lifetime of the network is increased by 26% over SIMPLE, M-ATTEMPT, and EERP.

Due to the increasing dependence on mobile devices worldwide, web designers are starting to design websites that are suitable for mobile devices such as smartphones and tablets. The aim of this paper is to find out which structures of interface the mobile commerce websites should implement to meet the user’s viewing visual behaviors. This is achieved through the use of eye tracking technology to record and analyze the content of online booking websites designed by responsive web design rules and to assess whether there was a difference in the location of the attention areas when the webpage presents different object arrangements. The paper finds that there are no significant differences in the number of fixations at the location of the menu icon, and the gaze sequences are also the same. However, participants have significant differences in fixation time on the navigation menu lists with different presentation methods, whereas the gaze sequences are the same; on the other hand, there are no significant differences in the total gaze time when objects are arranged in different positions, but the gaze sequences on the regions of interest are different when the object arrangements are changed.

Autonomic computing was the term coined by IBM in 2001. The term autonomic computing was used to define the self-adaptable nature of the human body. According to IBM, the same self-adaptable feature was the need to be incorporated in the software systems. Autonomic computing is the combination of few self-capabilities such as self-configuration, self-healing, self-optimization, self-protection, self-awareness, etc. So, autonomic computing approach was then used to develop autonomic software systems. This approach makes the computing systems self-adaptable and self-decision-making support systems for various activities. It also helps to reduce the human intervention in the software management process. Though, the implementation of autonomic self-capabilities may increase the software complexity, which further requires human intervention for the software maintenance-related specific tasks. Still, IT industries are approaching to develop autonomic features in their existing architecture or developing new self-adaptable software systems. Autonomic computing has its importance for providing a bridge for handling and managing the run-time computation-based issues/exceptions of the software. So, the discussion of this solution has become a necessity for making the vision of autonomic decision making more clear and understandable for researchers and developers for the improvement in an autonomic area. The paper provides an insight vision of the autonomic decision-making concept and its importance for the various purposes such as intrusion detection, cloud-based data security, wireless sensor network, Internet of Things, Big Data and many other areas where management cannot be handled by a human in real time. To assess the degree of autonomic feature, there is another term used which is known as autonomicity. The paper also discusses some solutions suggested and implemented by different researchers during their studies for estimating the system’s autonomicity level. These solutions will help in comparing different autonomic applications based on the autonomic features implemented in each application. This paper is an attempt to provide better understandability in the autonomic computational field.

This paper presents a new opportunistic scheduling and resource consolidation system based on an economic model related to different service level agreements (SLAs) classes. The goal is to address the problem of companies that manage a private infrastructure of machines, i.e., a cloud platform and would like to optimize the scheduling of several requests submitted online by users. For the sake of simplicity of the presentation, the proposed economic model has two SLAs classes (qualitative and quantitative) with three Quality of Service for each SLA class (Premium, Advanced and Best effort). The consequence of this choice as well as the need to serve requests as they come have an impact on the algorithmic ways to consolidate an infrastructure. Indeed, our system proposes a new allocation heuristic that adapts the number of active machines in the cloud according to the global resources usage of all machines inside the infrastructure. This heuristic can be examined as a consolidation heuristic, based on the idea that the system can make reasonable choices, based on the SLAs, for the placement and the allocation of resources for each request. Experimentation with our system is conducted on Prezi (Web workload) and Google Cloud Data (HPC-oriented workload) traces, and they demonstrate the potential of our approach under different scenarios. From a methodological point of view, we propose a general framework which is limited in scope, for the sake of simplicity in reading the paper, with a small number of SLAs, but the idea can be extended to many more SLAs and performance metrics. In this way, the user or the provider operating the cloud have more latitude, thanks to our multi-criteria approach, to control the workload without a sacrifice on performance.

Our paper aims at learning from imbalance data based on ensemble learning. At the stage, the main solution is to combine under-sampling, oversampling or cost sensitivity learning with ensemble learning. However, these feature space-based methods fail to reflect the transformation of distribution and are usually accompanied with high computational complexity and risk of overfitting. In this paper, we propose a dynamic cluster algorithm based on coefficient of variation (or entropy), which learns the local spatial distribution of data and hierarchically clusters the majority. This algorithm has low complexity and can dynamically adjust the cluster according to the iteration of AdaBoost, adaptively synchronized with changes caused by sample weight changes. Then, we design an index to measure the importance of each cluster. Based on this index, a dynamic sampling algorithm based on maximum weight is proposed. The effectiveness of the sampling algorithm is proved by visual experiments. Finally, we propose a cost-sensitive algorithm based on Bagging, and combine it with the dynamic sampling algorithm to propose a multi-fusion imbalanced ensemble learning algorithm. In experimental research, our algorithms have been validated on three artificial datasets, 22 KEEL datasets and two gene expression cancer datasets, and have shown ideal or better performance than SOTA in terms of AUC, indicating that our algorithms are not only effective imbalance algorithms, but also provide potential for building a reliable biological cyber-physical system.

The resources in the edge cloud are numerous and complex, and elastic scaling services can make efficient use of these resources. However, the elastic scaling services need to suspend the user’s application tasks forcibly when carrying out resource redistribution, which brings a poor sense of experience to the user. In view of the above problems, a dynamic elastic scaling model based on load prediction is proposed, which improves resource utilization and reduces scaling costs without affecting user experience. The model is divided into two parts. In terms of load prediction, on the one hand, according to the historical features and current trends of the load, the load prediction model based on the improved cloud model is used to predict the load demand at the next moment. On the other hand, the correlation between CPU and memory is considered. In terms of elastic scaling, integer programming algorithm is proposed to expand and release the corresponding resources with the minimum cost of horizontal scaling (HS) and vertical scaling (VS). In order to verify the superiority of elastic scaling model based on load prediction, corresponding comparative experiments are conducted, which show that the proposed model can improve the accuracy of load prediction and resource utilization with low scaling costs. Especially, the cost of elastic scaling proposed by this paper is lower than horizontal and vertical scaling. Compared with HS, the elastic scaling method proposed in this paper reduces the cost by 14%. Compared with VS, this method reduces the cost by 11%.

Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are “disk cache,” “memory cache,” and “no cache” to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark.

The ultimate goal of modern cryptography is to protect the information resource and make it absolutely unbreakable and beyond compromise. However, throughout the history of cryptography, thousands of cryptosystems emerged and believed to be invincible and yet attackers were able to break and compromise their security. The main objective of this paper is to design a robust cryptosystem that will be suitable to be implemented in Internet of Things. The proposed cryptosystem is based on algebraic geometric curves, more specifically on Hermitian curves. The new cryptosystem design is called Hermitian-based cryptosystem (HBC). During the development of the HBC design, Kerckhoffs’s desideratum was the main guidance principle, which has been satisfied by choosing the Hermitian curves as the core of the proposed design. The proposed HBC inherits all the advantageous characteristics of Hermitian curve which are large number of points that satisfy the curve and high genus curves. The aforementioned characteristics play a crucial role in generating a large size encryption key for HBC and determine the block size of plaintext. Due to the fact that HBC used algebraic geometric codes over Hermitian curve, it has the ability to perform error correction in addition to data encryption. The error correction is another advantage of HBC compared with many existing cryptosystems such as McEliece cryptosystem. The number of errors that can be corrected by HBC is larger (high data rate) than other algebraic geometric codes such as elliptic and hyperelliptic curves. It also uses non-binary representation which increases its attack resistance. In this paper, the proposed HBC has been mathematically compared with elliptic curve cryptosystem. The results show that HBC has many advantages over the elliptic curves in terms of number of points and genus of the curve.

The increasing social problems on population, resources and environment enable the interaction between nature and humanity to become one of the most active research fields in the world. In this paper, we propose a novel framework of human–land sustainable computational system, which fully advances the progress of the development of our society utilizing the cloud computing and big data analysis technologies. Particularly, the study on quality of land management has attracted much attention. With the proposed framework, multi-user multi-cloud environment (MUMCE) is firstly presented, and evaluation of land quality is regarded as various services, such as soil acidity and alkalinity, soil thickness, soil texture, smoothness and field layout. Then, this paper formulates the problem of formal concept analysis-based multi-cloud composition recommendation with regard to multiple users. To address this problem, this paper first adopts the collaborative filtering to obtain the services request of the target user, then the service–provider concept lattices are constructed, and finally the best multi-cloud composition is selected and further recommended for the target user. Meanwhile, the corresponding algorithm is also devised. A case study is conducted for evaluating the feasibility and effectiveness of the proposed approach.

Dynamic reconfiguration provides flexibility in the design and management of reconfigurable computing (RC) systems such that numerous applications would be mapped into limited resources simultaneously. As the mapping is a computationally intensive procedure in application compilation, a low-complex method is needed strongly for RC applications. In this paper, we propose a link-elimination partitioning approach for application graphs to reduce computations and reach an optimal solution faster as well. The link-elimination preprocessing step is performed by investigating the standard deviation of weights and removing lightweight links from the partitioning procedure. Based on the Laplacian matrix, a formulation method for detecting high-degree nodes as partition seeds has been generated. Moreover, a distance model for the region of implementation in resource graph has been introduced in this paper. In order to select among any rectangular shape of the resource graph, an average distance factor has been defined analytically. It has been proved that partitions with more connectivity must be implemented in a square-formed shape. Extensive experiments with random and benchmark graphs have been carried out to compare the proposed partitioning approach with the previous methods, and the results manifested that for fixed searching iterations, quality of solutions and time overhead have been improved 22% and 59%, respectively.

Since scientific workflow scheduling becomes a major energy contributor in clouds, much attention has been paid to reduce the energy consumed by workflows. This paper considers a multi-objective workflow scheduling problem with the budget constraint. Most existing works of budget-constrained workflow scheduling cannot always satisfy the budget constraint and guarantee the feasibility of solutions. Instead, they discuss the success rate in the experiments. Only a few works can always figure out feasible solutions. These methods work, but they are too complicated. In workflow scheduling, it has been a trend to consider more than one objective. However, the weight selection is usually ignored in these works. The inappropriate weights will reduce the quality of solutions. In this paper, we propose an energy-aware multi-objective reinforcement learning (EnMORL) algorithm. We design a much simpler method to ensure the feasibility of solutions. This method is based on the remaining cheapest budget. Reinforcement learning based on the Chebyshev scalarization function is a new framework, which is effective in solving the weight selection problem. Therefore, we design EnMORL based on it. Our goal is to minimize the makespan and energy consumption of the workflow. Finally, we compare EnMORL with two state-of-the-art multi-objective meta-heuristics in the case of four different workflows. The results show that our proposed EnMORL outperforms these existing methods.

Multi-robot task allocation problem is an important research field in multi-robot systems. The multi-robot task allocation problem combined with affective computing is an interesting frontier problem. This paper proposes a distributed affective robot pursuit task allocation algorithm based on self-awareness, which combines cognitive intelligence with emotional intelligence to establish self-awareness for affective robots. This paper defines the cognitive mechanism, environmental detection mechanism and self-decision mechanism of affective robots, so that the self-aware affective robots can make decisions autonomously by grasping the information of itself and the environment. The experimental results show better efficiency of this task allocation algorithm at each task scale, and the allocation efficiency is gradually improved as the number of robot increases.

Appropriate irrigation planning with reference evapotranspiration (ETo) plays an important role in managing the water resources for agriculture. This paper presents machine learning models for predicting ETo and thereby aiding in irrigation planning. Daily weather data which include minimum and maximum temperature, relative humidity, solar radiation, soil temperature and wind speed were used to forecast ETo. The datasets were trained, validated and tested using multiple linear regression, random forest and gradient boost regression (GBR) algorithms. The results of the machine learning algorithms with and without the proposed preprocess techniques were compared. Performance evaluations of these models were done by comparing the root mean square error, mean absolute error and coefficient of determination. It was found that the preprocessed GBR model edges past the other two models in predicting the ETo. The paper also discusses the mechanism of irrigation planning with the help of ETo, crop evapotranspiration (ETc) and irrigation system efficiency.

Scientific applications are some of the most computationally demanding software pieces. Their core is usually a set of linear algebra operations, which may represent a significant part of the overall run-time of the application. BLAS libraries aim to solve this problem by exposing a set of highly optimized, reusable routines. There are several implementations specifically tuned for different types of computing platforms, including coprocessors. Some examples include the one bundled with the Intel MKL library, which targets Intel CPUs or Xeon Phi coprocessors, or the cuBLAS library, which is specifically designed for NVIDIA GPUs. Nowadays, computing nodes in many supercomputing clusters include one or more different coprocessor types. To fully exploit these platforms might require programs that can adapt at run-time to the chosen device type, hardwiring in the program the code needed to use a different library for each device type that can be selected. This also forces the programmer to deal with different interface particularities and mechanisms to manage the memory transfers of the data structures used as parameters. This paper presents a unified, performance-oriented, and portable interface for BLAS. This interface has been integrated into a heterogeneous programming model (Controllers) which supports groups of CPU cores, Xeon Phi accelerators, or NVIDIA GPUs in a transparent way. The contribution of this paper includes: An abstraction layer to hide programming differences between diverse BLAS libraries; new types of kernel classes to support the context manipulation of different external BLAS libraries; a new kernel selection policy that considers both programmer kernels and different external libraries; a complete new Controller library interface for the whole collection of BLAS routines. This proposal enables the creation of BLAS-based portable codes that can execute on top of different types of accelerators by changing a single initialization parameter. Our software internally exploits different preexisting and widely known BLAS library implementations, such as cuBLAS, MAGMA, or the one found in Intel MKL. It transparently uses the most appropriate library for the selected device. Our experimental results show that our abstraction does not introduce significant performance penalties, while achieving the desired portability.

The paradigm of computing has shifted from computation-centric to communication-centric designs. Network-on-chip has emerged as an alternative interconnect mechanism for future multi-core designs. Transistor integration is approaching its limit, and this increases the susceptibility of the interconnects toward failures. Research efforts are directed toward improving the fault tolerance of these interconnects. Fault-tolerant routing such as segment-based and up*/down* are static by nature and require reconfiguration to circumvent failures. Failures may disrupt the connectivity of the network, and new routing instance needs to be configured in case old routing instance is unable to offer full connectivity. In this paper, we identify nodes affected by the failures and propose an extended scalable routing reconfiguration, called S2DIO. It performs reconfiguration of affected nodes by taking $$N^2$$ cycles for $$N \times N$$ mesh network, whereas state-of-the-art (ARIADNE) consumes $$N^4$$ cycles. Instead of routing tables, we employ logic-based routing and achieve significant improvements, i.e., $$30.7\%$$ in terms of area and $$29\%$$ in terms of power overhead for a $$16\times 16$$ mesh router. A novel algorithm for computation of new logic-based routing bits is also proposed in this paper. Our reconfiguration (S2DIO) improves average flit latency up to $$32\%$$ and throughput up to 19% for single-link failure in $$8\times 8$$ 2D mesh network-on-chip.

Since Osman Abul et al. first proposed the k-anonymity-based privacy protection for trajectory data, researchers have proposed a variety of trajectory privacy-preserving methods. These methods mainly adopt a static anonymity algorithm, which only focusing on the trajectories in a specific time span, directly anonymizes data and publishes them without considering dynamic nature of trajectory data as the new time slice arriving. Furthermore, due to its correlation with time and position, the trajectory data is produced in large scale and many sensitive attributes; the traditional k-anonymity-based privacy-preserving models need to recalculate the last released trajectory data, which will increase the computing cost and reduce the availability of the released trajectories, are not fit for privacy protection in large-scale trajectory data. Therefore, this paper presents a method to dynamically publish the large-scale vehicle trajectory data with privacy protection under $$(k,\delta )$$ security constraints. According to the spatial and temporal characteristics of vehicle trajectory data, this paper first proposes a method to partition the trajectory data for storage and computation. We choose the sample point $$(x_{i},y_{i})$$ at time $$t_{i}$$ as partition points and store the partitions of the trajectory data according to the time sequence and location of the running vehicle. This results in the efficient trajectory scanning, clustering and privacy protection. We use $$(x_{i},y_{i},t_{m}-t_{n})$$ to represent the identifier of trajectory data to publish, use the generalize function to cluster trajectory data under the $$(k,\delta )$$ security constraints. Through this way, we can effectively process the trajectory in every data partition as time goes on and need not to recalculate the released trajectories, effectively reduce the computing cost. Through experiments on real trajectory data and Oldenburg trajectory data, confirming the data partitioning method in privacy-preserving large-scale trajectory data publishing under the security constraint of $$(k,\delta )$$, and the l-diversity. By the experimental comparison, our method maintains a least level of computing cost and higher data availability.

This paper presents an internet-of-things (IoT)-cloud converged virtual machine (VM) system for IoT devices with restricted computing resources. The VM is a software processor that has many advantages in terms of software development, release, maintenance, etc., owing to its platform independence. However, in low-performance devices it has a significant disadvantage because its use is restricted by high execution overheads at the software interpretation level. This paper proposes a VM system for IoT devices that solves this problem while retaining the advantages of VM technology using a lightweight interpreter model and cloud-based computation offloading. The proposed interpreter solves the limited memory/performance problem when running VMs on low-performance devices using two-level instruction-to-native function matching techniques. Furthermore, by solving the low-performance issues of IoT devices using cloud-based offloading, the proposed IoT-cloud VM can run applications that require high-performance computing even when the target hardware system is a low-power IoT device.

In the domain of proteomics, an in-depth analysis of the 3D structure of a protein is of paramount importance for many biological studies and applications. At the secondary level, protein structure can be described in terms of motifs, recurrent patterns of smaller biological structures called secondary structure elements. In this paper, the focus is on the identification of geometrical motifs in different proteins using the Cross Motif Search (CMS) algorithm. Such task, due to the high computational cost of CMS with respect to traditional alignment algorithms, is very demanding, and thus, parallel processing is mandatory. In previous papers, CMS parallelization has been already studied from the HPC standpoint. Since cloud computing is emerging as an alternative to on-premise HPC systems, it is worthwhile examining the feasibility and possible advantages in terms of both performance and costs, of migrating to a cloud implementation. This paper is an extension of a preliminary work carried out on the cloud parallelization of CMS. The paper has two main contributions. First of all, an analytic model of the communication pattern of CMS is described, in order to get insights on the performance of the application when executed on a cloud infrastructure. Secondly, an optimized “location-aware” scheduling policy to assign workload to the application workers is introduced, in order to minimize internode communication in a cloud setting. Experiments are presented in order to validate the newly introduced scheduling policy and assess the performance of the cloud implementation of CMS. The results presented in this paper are general, in the sense that they can be applied to any other algorithm with a communication pattern similar to the one of the target applications.

This paper proposes a smart machine-learning-based unmanned aerial vehicle (UAV) control system that optimizes UAV operations in real time. The purpose of this system is to increase the efficiency of UAVs that need to operate with limited resources. This can be accomplished by allowing the UAVs in flight to identify their current state and respond appropriately. The proposed system, which is developed based on “cloud robotics,” benefits from the powerful computational capabilities of cloud computing and can therefore calculate many types of information received from various sensors in real time to maximize the performance of the UAV control system. The system learn about normal situations when creating models. That is, preprocessing data that is correlated with a particular situation and modeling it with a “multivariate Gaussian distribution.” Once the model is created, the UAV can be used to analyze the current situation in real time during flight. Of course, it is possible to recognize the situation based on the traditional RULE or the latest LSTM. However, this is not an appropriate solution for UAV situations where irregularities are severe and unpredictable. In this paper, we succeeded in recognizing the UAV flight status in real time by the proposed method and succeeded in optimizing it by adjusting communication cycle based on a recognized situation. Based on the results of this study, we expect to be able to stabilize and optimize systems that are highly irregular and unpredictable. In other words, this system will be extended to learn about various situations and create a model. A reliable and efficient smart system can be designed by judging the situation comprehensively.

The hypercube is one of the most popular interconnection networks. Its network cost is $$O(n^2)$$ . In this paper, we propose a new hypercube variant, the divide-and-swap cube $$\textit{DSC}(n)\,(n=2^d,\,d\ge 1)$$ , which reduces the network cost to $$O(n \log n)$$ while maintaining the same number of nodes and the same asymptotic performances for fundamental algorithms such as the broadcasting. The new network has nice hierarchical properties. We first show that the diameter of $$\textit{DSC}(n)$$ is lower than or equal to $$\frac{5n}{4}-1$$ . However, unlike the hypercube of dimension n whose degree is n, the node degree of the network is $$\log n + 1$$ , resulting in a network cost of $$O(n \log n)$$ . We then examine the one-to-all and all-to-all broadcasting times of $$\textit{DSC}(n)$$ , based on the single-link-available and multiple-link-available models. We also present an upper bound on the bisection width of the $$\textit{DSC}(n)$$ and show that $$\textit{DSC}(n)$$ is Hamiltonian. Finally, we introduce the folded divide-and-swap cube, $$\textit{FDSC}(n)$$ , a variant of the $$\textit{DSC}(n)$$ and study its many properties including its hierarchical structure, routing algorithm, broadcasting algorithms, bisection width, and its Hamiltonicity. All the broadcasting algorithms presented in this paper are asymptotically optimal.

This paper presents the development of artificial neural network (ANN) model for predicting the dynamic across-wind response of tall building as per the provisions given in Indian Wind Code (IWC), IS 875 (Part-3):2015. IWC has given the procedure for estimating across-wind load response for limited aspect ratios of the building. For calculating the across-wind response not conforming to the ratios specified in the code, additional time-consuming and expensive studies are required. In order to minimise such tedious studies, an efficient computational technique is essential to determine wind response. ANN proves to be a promising alternative technique for analysing complex engineering problems. Thus, in this paper, using the best-fit model of ANN, predictions are made for the building configurations that are not included in IWC. The results are plotted as a series of charts in terms of base shear and the base bending moment. It is found that the predictions made using the trained and developed ANN models are in good agreement with the desired output.

This paper concentrates on the entropy estimation of time series. Two new algorithms are introduced: Fast Approximate Entropy and Fast Sample Entropy. Their main advantage is their lower time complexity. Examples considered in the paper include interesting experiments with real-world data obtained from IT4Innovations’ supercomputers Salomon and Anselm, as well as with data artificially created specifically to test the credibility of these new entropy analyzers.

Most of the wireless sensor networks (WSNs) are equipped with battery-powered devices with limited processing/communication resources which necessitate the designed algorithms to reduce the computational burden on nodes as much as possible. This could be achieved using distributed algorithms in which the computations are distributed between all of the nodes. In WSNs, the sensors observe phenomena that could be common to some of them, in addition to the intra-signal correlation; therefore, the acquired signals by the sensors possess some inter-signal correlation. These joint structures could be exploited if the designed algorithms are cooperative. On this basis, in this paper a new distributed and cooperative signal recovery algorithm in the application of compressive sensing for WSNs is proposed. We consider a situation that the sensor nodes intend to recover signals that differ from one sensor to another, in the underdetermined systems of equations, while these signals have common sparsity profile. In fact, we introduce a general structure which can be applied to many optimization problems with different non-convex objective functions and different constraints. In this paper, this method will be used on three problems with different constraints and will result in three completely distributed methods. The simulation results show that the proposed method presents better performance compared to the other existing algorithms in terms of both recovery and convergence rate.

An electromagnetic flow sensor applied to the flow measurement of iron ore pipeline transportation will incur a measurement error after being used for a period of time, and the error will increase continuously. In this paper, based on the weight function theory of electromagnetic flow sensors, the main cause of the error is proposed. Specifically, in the process of using the electromagnetic flow meter the insulation lining of the sensor is easily damaged because the measuring pipe of the sensor constantly collides with the solid phase of the fluid, leading to a change in the distribution of the weight function of the electromagnetic flow sensor, which, in turn, changes the contribution of the induced electromotive force generated by each medium in the pipe to the measuring electrode. This paper is based on the PDE computer numerical calculation method, and, according to the analysis of the weight function under different boundary conditions, the rule that the output signal of the electromagnetic flow sensor decreases with increasing insulation lining area is obtained. The computer numerical calculation method used in this paper can provide a new approach to the theoretical analysis of the weight function of electromagnetic flow sensors. Furthermore, the conclusion reached in this paper can provide a theoretical basis for the development and maintenance of electromagnetic flow sensors applied to the flow measurement of iron ore pipelines.

The load balancing algorithm of web server cluster in network education system is studied in this paper, and a new strategy based on server status and request information is proposed to reduce the response time. The mechanism of the algorithm is to classify the degree of influence of the client’s request content on the server, and set the corresponding weight. At the same time, it combines the server performance parameters and load conditions to allocate the request to the lightest load of the server. The overall framework structure of the hierarchy is proposed according to the MOOC education system, and the internal communication mechanism of the system and the monitoring program of the web server performance are studied in this paper. Experimental results show that the algorithm can fully utilize the existing software and hardware resources, effectively balance the load of the web cluster server, and fully utilize the service resources.

The DVM and SAPFOR systems were primary designed to simplify the development of parallel programs of scientific-technical calculations. DVM includes CDVMH and Fortran-DVMH programming languages which are the extensions of standard C and Fortran languages by parallelism specifications. The DVMH programs can be efficiently executed on heterogeneous computational clusters with accelerators. SAPFOR is a software development suit that can be used to produce a parallel version of a sequential program in a semiautomatic way, according to DVMH model of the parallel programming. The paper considers the application of DVM and SAPFOR systems at all stages of the program parallelization on the example of mapping of 3D elastic waves simulation method on high-performance heterogeneous clusters with GPUs. SAPFOR enables us to investigate original sequential program. Then, Fortran-DVMH language is used to exploit parallelism. Tools for functional and performance debugging, that are part of the DVM system, allow to resolve errors in the parallel program and to improve its performance. The paper also compares the DVMH-based program with a program obtained after manual parallelization using MPI programming technology.

On the basis of Wyner–Ziv video coding in transform domain, this paper studies the coding method of key frame and proposes a method of coding mode selection based on partition of frequency band: First of all, the key frames are transformed by DCT and they are divided into low-frequency band and high-frequency band according to the characteristics of frequency band. Then the low-frequency band and high-frequency band are encoded and decoded by Wyner–Ziv and intraframe, respectively. In this paper, the decision of coding mode is transferred to the decoder to be completed, which further improves the performance of the system without increasing the complexity of the encoder. Compared with the reference algorithm, the peak signal-to-noise ratio obtained by the proposed coding mode selection algorithm based on partition of frequency band is improved by 1–5 dB.

Supply chain (SC) management plays a crucial role in modern enterprises, especially for smart manufacturing enterprises. It refers to the coordination and integration with upstream suppliers and downstream customers, aiming at optimizing the performance of the entire SC of smart manufacturing enterprises. Owing to the complexity and dynamics of the SC system and the multitudinous uncertainties of the changing business environment, it is necessary for smart manufacturing enterprises to have a good SC organization and operation model to coordinate the benefits of actors involving in the network system, to improve the information flow and logistics, and to make rapid response to the changing market and industry. For these aspects, this paper attempts to utilize the agent technology to optimize the SC, promoting its capability of dynamic allocation planning. Further, smart manufacturing enterprises will be taken into account in this paper to make specific analysis. The results show that the application of agent technology will contribute to the coordination and dynamic allocation planning, and the agent-based SC dynamic allocation planning will be conducive to the comprehensive performance of the smart manufacturing enterprises, which has extensive managerial significance in practices.

To make fractal endpoint detection algorithm, to maintain good performance and to deal with noise with higher irregularity than speech, fractal endpoint detection algorithm based on frequency domain was proposed in the paper. The frequency domain represented energy distribution and signal, and the speech harmonic component had very strong periodicity and regularity in the frequency domain. Thus, method of extracting fractal dimension after converting to short-time frequency domain had better robustness. Analysis means were introduced based on short-time frequency domain fractal against the existing fractal algorithm. Its stability was due to the frequency domain represented frequency domain energy distribution of signal and the speech signal energy mainly focused on harmonic. Thus, solving fractal dimension in short-time frequency domain could weaken the impact of different types of noises. Compared with time-domain fractal, the threshold value of short-time frequency domain fractal was more stable and the judgment criterion direction was fixed, smaller than the represented speech fragment of threshold value. Frequency domain was used for representing the signal energy distribution characteristics and the strong periodicity and regularity of speech harmonic component so as to extract fractal dimension and distinguish speech and noise. Thus, the fractal dimension extraction method after converting to short-time frequency domain proposed in the paper had better robustness. Not only is it applicable to irregular white noise, but also applicable to noises with stronger time-domain periodicity and regularity including tank noise.

In recent years, energy efficiency has emerged as one of the most important design requirements for modern computing systems, ranging from single servers to data centers and Clouds, as they continue to consume an enormous amount of electrical power. Cloud computing can be used to achieve energy efficiency through efficient task scheduling in the distributed environment. This efficient task scheduling helps to improve resource utilization, which, in turn, helps to minimize energy consumption. In this paper, we work toward minimizing energy of directed acyclic graph-structured applications on heterogeneous cloud system. The paper also combines power-aware list-based scheduling algorithm with dynamic voltage and frequency scaling (DVFS) technique for real-time tasks (PL-DVFS) to maintain the quality of service while considering tasks deadlines. The goal of the approach is to improve performance and overall reduced energy consumption comprising CPU energy (busy and idle) and communication energy. Experiments conducted with synthetic workflow graphs clearly demonstrate the advantage of the proposed approach.

In the digital design area, quantum-dot cellular automata (QCA) has become a promising alternative to the CMOS technology. As a basic unit in digital arithmetic circuits, the full adder has been extensively studied in the development of QCA technology. This paper presents two novel full adder implementations using QCA, which outperform other designs with fewer cells, smaller areas, shorter latency and lower cost. The two full adders share many properties in common and differ only in cell numbers. Concretely speaking, a latency of 0.75 clock cycle, area of 0.01 μm2 and cost weighted 0.0056 is implemented using only 28 and 31 normal cells, respectively. To illustrate the superiority of our design in complex structures, ripple carry adder circuits of 4-bit, 8-bit and 16-bit size have been implemented using the proposed 1-bit full adder. Simulation results show that the proposed design also has good stability and scalability in different circuit size, resulting in significant improvements in terms of number of cells, area, cost compared to designs in other studies, while maintaining an equally well clock latency with the best previous one. The proposed designs in this paper have been functionally verified with the QCADesigner tool.

This paper proposes an energy-efficient dynamic decision model for wireless multi-sensor network, which is based on the dynamic analysis of the energy consumption characteristics of wireless multi-sensor nodes. We analyze the behaviors of the nodes in wireless multi-sensor network and introduce the existing energy-efficient decision methods, then propose a simple dynamic decision model and prove it theoretically. This paper uses MATLAB 2015 to carry out simulation experiments under the condition of fixed routing protocol based on tree topology and two low-power routing protocols based on mesh topology, and simulation results show that the network lifetime is obviously prolonged. Extending the application of the proposed decision model to aquaculture environmental monitoring system, testing results show that the proposed decision model can effectively reduce the network energy consumption, and be promising when generalized to other applications.