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The Sunway TaihuLight is the first supercomputer built entirely with domestic processors in China. On Sunway Taihulight, the local data memory (LDM) of the slave core is limited, so data transmission with the main memory is frequent during calculation, and the memory access efficiency is low. On the other hand, for many scientific computing programs, how to solve the storage problem of irregular access data is the key to program optimization. Software cache (SWC) is one of the effective means to solve these problems. Based on the characteristics of Sunway TaihuLight structure and irregular access, this paper designs and implements a new software cache structure by using part of the space in LDM to simulate the cache function, which uses new cache address mapping and conflicts solution to solve high data access overhead and storage overhead in a traditional cache. At the same time, the SWC uses the register communication between the slave cores to share on the different slave core LDMs, increasing the capacity of the software cache and improving the hit rate. In addition, we adopt a double buffer strategy to access regular data in batches, which hides the communication overhead between the slave core and the main memory. The test results on the Sunway TaihuLight platform show that the software cache structure in this paper can effectively reduce the program running time, improve the software cache hit rate, and achieve a better optimization effect. [ABSTRACT FROM AUTHOR]

Mission-critical applications refer to the real-time applications, which require fast and secure service provisioning, such as defense sector and disaster management. This paper proposes a delay-aware and secure service provisioning model for such types of applications. As a use-case, we have considered the defense sector, which is a vital sector for a country's all-round well-being including security, safety, society, and economy. In the conventional sensor-cloud model, the sensor data is stored and processed in the cloud. However, the sensor nodes have small coverage and the use of the long distant cloud servers increases the delay. Therefore, the conventional sensor-cloud model may not be efficient for defense application. Moreover, data hiding for security purposes is another important aspect of this field. To address these challenges, this paper proposes a mobility-aware sensor-fog paradigm for mission-critical applications based on network coding and steganography, referred to as Mobi-Sense. In Mobi-Sense, steganography is used for hiding the data during transmission. The theoretical results demonstrate that Mobi-Sense outperforms the existing frameworks with respect to delay and power consumption by ∼ (40 - 80) %. The simulation results present that Mobi-Sense reduces the delay by ∼ (18 - 40) % than the conventional sensor-cloud framework for mission-critical applications. An optimal path finding algorithm based on deep learning has been deployed in the context of disaster scenario. The experimental analysis shows that the proposed optimal path finding method achieves precision and accuracy above 90%. This is observed that our proposed modules have outperformed existing baselines in terms of accuracy, delay, and power consumption. [ABSTRACT FROM AUTHOR]

Medical data are increasing drastically due to the vast development of medical sciences. The security of this immense data is also a challenge of the present era. Image watermarking is a technique to secure medical data from alteration. Authentication of patients records is also necessary in case of medical data transmission. In this paper, an optimized electroencephalogram watermarking technique are proposed with dual authentication using Advanced encryption standards (AES) and speeded-up robust features is proposed. Scaling factor plays an important role to balance the properties of watermarking algorithm. The cuckoo search optimization is used to get the optimized scaling factor. The Henon encryption (HE) is used to enhance the security of sub-band obtained from identity of the patient image used as the watermark. The diagonalized Hessenberg decomposition (HD) is used for embedding watermark while secured hash algorithm (SHA-256) is used to protect watermark against malicious attacks. For the proposed technique, detailed security analysis has been performed for AES encryption technique. Various performance metrics are computed for the proposed technique to estimate the effectiveness of the watermarking system. [ABSTRACT FROM AUTHOR]

The convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity, which involve enormous communication bandwidth and storage resources requirement. The computation requirement can be addressed effectively to achieve high throughput by highly parallel compute paradigms of current CNNs accelerators. But the energy consumption still remains high as communication can be more expensive than computation, especially for low power embedded platform. To address this problem, this paper proposes a CNNs accelerator based on a novel storage and dataflow on multi-processor system on chip (MPSoC) platform. By minimizing data access and movement and maximizing data reuse, it can achieve the energy efficient CNNs inference acceleration. The optimization strategies mainly involve four aspects. Firstly, an external memory sharing architecture adopting two-dimensional array storage mode for CPU-FPGA collaborative processing is proposed to achieve high data throughput and low bandwidth requirement for off-chip data transmission. Secondly, the minimized data access and movement on chip are realized by designing a multi-level hierarchical storage architecture. Thirdly, a cyclic data shifting method is proposed to achieve maximized data reuse based on both spatial and temporal. In addition, a bit fusion method based on the 8-bit dynamic fixed-point quantization is adopted to achieve double throughput and computational efficiency of a single DSP. The accelerator proposed in this paper is implemented on Zynq UltraScale + MPSoC ZCU102 evaluation board. By running the benchmark network of VGG16 and Tiny-YOLO on the accelerator, the throughput and the energy efficiency are evaluated. Compared with the current typical accelerators, the proposed accelerator can increase system throughput by up to 41x, single DSP throughput by up to 7.63x, and system energy efficiency by up to 6.3x. [ABSTRACT FROM AUTHOR]

Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance. [ABSTRACT FROM AUTHOR]

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time. [ABSTRACT FROM AUTHOR]

As multi/many-core processors are widely deployed in high-performance computing systems, efficient intra-node communication becomes more important. Intra-node communication involves data copy operations to move messages from source to destination buffer. Researchers have tried to reduce the overhead of this copy operation, but the copy operation performed by CPU still wastes the CPU resources and even hinders overlapping between computation and communication. The copy engine is a hardware component that can move data between intra-node buffers without intervention of CPU. Thus, we can offload the copy operation performed by CPU onto the copy engine. In this paper, we aim at exploiting copy engines for MPI blocking collective communication, such as broadcast and gather operations. MPI is a messaging-based parallel programming model and provides point-to-point, collective, and one-sided communications. Research has been conducted to utilize the copy engine for MPI, but the support for collective communication has not yet been studied. We propose the asynchronism in blocking collective communication and the CE-CPU hybrid approach to utilize both copy engine and CPU for intra-node collective communication. The measurement results show that the proposed approach can reduce the overall execution time of a microbenchmark and a synthetic application that perform collective communication and computation up to 72% and 57%, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a key concept of the sixth generation (6G) mobile network. In the 6G paradigm, NOMA is a significant energy and spectral efficiency technique, especially in the indoor region, to maintain the quality of service. multiple input multiple outputs (MIMO) base station serves as the main base station due to massive connectivity. In MIMO, spatial modulation (SM) gives a huge data rate and reduces inter-antenna interference. Beamforming permits a proficient intercell interference organization for short-distance data transmission. The prime concept in NOMA is to provide various end users with the same resource block based on a time frame, spreading code, or subcarrier successive interference cancellation. In this article, we propose a power-efficient 6G NOMA-beamforming-based spectrum allocation scheme for the Internet of Things (IoT) devices in the indoor region. MIMO with SM transmits the signal to indoor next-generation node B (gNB). It is a 3GPP-complaint implementation of 5G/beyond 5G base stations. Various IoT devices are connected with those gNBs and share the communication resources. In this paper, we propose an IoT device-interoperability scheme based on the requirements of the IoT devices aiming at improving the network performance. The simulation results show that the proposed model diminishes power consumption and improves signal-to-interferences-plus-noise ratio. [ABSTRACT FROM AUTHOR]

With the rapid development of Vehicular ad hoc networks (VANETs), secure communication in VANETs has been extensively studied. However, it is very challenging for VANETs to establish reliable data sharing for one-to-many users without access control. Attribute-based encryption (ABE) is a promising access control technique, which can maintain data confidentiality and is suitable for applications in VANETs. The current ABE scheme performs encryption and decryption of elements on the bilinear group, which is unfriendly for large-scale plaintexts and causes VANETs vulnerable to receive malicious or erroneous information. In this paper, we propose a variant attribute-based encryption access control scheme to achieve fine-grained access control for VANETs data shared in cloud and fog. It allows a message sender to determine which vehicles receive and decrypt messages based on the vehicle's attributes. We also employ a fusion of AES and CP-ABE to address the issue of key leakage caused by symmetric encryption key transferring. We implement a one-time key operation to make each ciphertext with its unique key and signature, which greatly improves the security of data transmission and provides tamper-proof for the plaintext. We also offer a novel method that treats roadside units as the constituent parts of a blockchain and publishes the device's public signature on the chain to validate the sender's identity. Compared with other existing CP-ABE schemes, our scheme is easily retrofitted to other encryption frameworks. Finally, performance evaluation reveals the security and efficiency of the proposed scheme based on theoretical analysis and experimental analysis. [ABSTRACT FROM AUTHOR]

Reducing network energy consumption and balancing workload are two key optimization goals for data transmission in edge computing field. However, these two goals are likely to be conflicting in some cases and fail to achieve the optimum simultaneously. In this paper, we design a new data transmission optimization algorithm using multi-objective reinforcement learning. We design the vector of rewards for the two objectives, and update Pareto approximate set by multiple state steps to approach the optimal solution. In every step, we classify the candidate links into four different levels for path selection. We aggregate network traffic to construct minimum topology subset, minimizing the number of occupied device to reduce energy consumption. We optimize the load distribution on those selected links, minimizing maximum congestion factor to balance workload. For action selection, we leverage roulette-based Chebyshev scalarization function to solve the weight selection problem for multi-objectives and enforce exploration to avoid falling into local optimum. To improve the convergence rate, we design heuristic factor to control the search of solution space and enhance the guiding effect of the existing optimal solution. Simulation result shows that the proposed algorithm achieves good performance in energy-saving and load balance at the same time. [ABSTRACT FROM AUTHOR]

Hash algorithms are among the most crucial algorithms in cryptography. The SM3 algorithm is a hash cryptographic standard of China. Because of the strong collision resistance and irreversibility of hash algorithms, they are widely used as a basic function in various fields such as digital signatures and random number generation. With the increasing real-time applications of automation in the fields of finance and office, the network puts forward higher demands for the implementing efficiency of the SM3 algorithm. We present a CUDA-based parallel optimized method for SM3 algorithm by four different ways: They are Single data stream with Single thread (SS), Multiple data streams with Single thread (MS), Single data stream with Multi-thread (SM), and Multiple data streams with Multi-thread (MM). The experimental result shows MM is the best of the four. When considering the data transmission between CPU and GPU, the proposed optimized algorithm achieves a peak performance of 166.42 Gb/s, which is 1.96 times of the best-known implementation of the SM3 algorithm on GPU platforms. Without transmission time counting, the peak performance is near 8500 Gb/s. Compared with other SM3 GPU algorithms, the algorithm proposed in this paper significantly enhances the efficiency of digest generation. Furthermore, the results show a new conclusion that the optimization of logical operations in the SM3 algorithm has reached a very high extent and the data transmission of PCIE becomes the bottleneck in the CPU+GPU data processing mode. Therefore, future work on the optimization of the SM3 algorithm should pay more attention to the PCIE data transfer efficiency. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) is a type of wireless sensor network that plays a crucial role in monitoring patient healthcare. In this network, sensor nodes are typically placed inside or outside the patient's body. These nodes are capable of transmitting data to the sink node when any functional changes in the patient are detected. The routing and energy efficiency of the network nodes are challenging tasks in WBAN, as each sensor node operates on a battery. Therefore, in multi-hop routing designing a robust routing protocol has a significant impact on reducing energy consumption during the selection of the next hop. In this paper, a simple novel routing protocol named simple energy-aware and reliable (SEAR) routing protocol is proposed to transmit reliable data packets in a WBAN. The proposed routing scheme considers the remaining energy of sensor nodes, priority data, and hop count to the sink node as significant metrics for dynamically selecting the best forwarder node. Furthermore, the proposed protocol utilizes the route reliability factor (RRF) to select the optimal route among all possible routes between the source sensor node and the sink node. RRF selects the route with the maximum route residual energy and minimum hop count. As a result, SEAR has the ability to provide effective single-hop and multi-hop routing data transmission to improve the reliability of data transmission, decrease the energy consumption of the sensor nodes, and prolong the lifetime of the network. The simulation results show that the performance of the SEAR routing protocol outperforms the existing routing protocols for the metrics: packet loss ratio, throughput, end-to-end delay, normalized routing load, energy consumption, and network lifetime. The results indicate that the proposed protocol improves total energy consumption by 18.76% and 10.89% when compared to EERR-RLFL and AMCRP respectively. Meanwhile, SEAR reduces the average end-to-end delay by 17% and 9%, packet loss ratio by 29.48% and 17.69%, and normalized routing load by 31.17% and 20.91%. Additionally, SEAR achieves up to 16% and 9.71% higher throughput compared to EERR-RLFL and AMCRP routing protocols respectively. Overall, the results obtained indicate that the proposed SEAR routing scheme significantly enhances the overall performance of the network. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are most used to monitor remote environments. Multitudinous sensor nodes gather data in a self-governing manner, operating on an exhaustive source of energy or battery. Clustering process structures the network into a hierarchy wherein sensor nodes gather data passed to selected cluster head nodes which perform data processing, aggregation, and transfer it to a base station. Prolonging network lifetime and enhancing total data transmission to base station are major challenges in wireless sensor network and same is addressed in this work. A distributed energy-based epoch is used in this paper to determine node eligibility to become cluster head and a multi-parameter-weighted scalarization function is proposed to determine best cluster head candidates in order to manage dynamic and multi-characteristic node heterogeneity. The parameters used are distance to base station, expected cluster head lifetime, average cluster member node lifetime and maximum power consumed by a cluster member node. A novel weight computation strategy using analytical hierarchy process is introduced in this paper which enhances the optimality of scalarization function value. The proposed algorithm is distributed over two phases as network setup phase and clustering phase. The network setup phase computes the energy model and optimal number of cluster heads. The second phase proposes the cluster head selection process using weight-based scalarization and introduces the novel weight selection method. Finally, network operation enters the data transmission phase. The results show an enhancement in throughput at base station, with an increase of close to 30% along with an increase in the network lifetime of up to 20% as measured by last node death. The simulation results are produced in comparison with the considered base protocol of DEEC as well as other protocols using similar concepts for implementation. However, utilization of a two-step cluster heads selection process including unique node epochs for shortlisting and scalarization function-based node fitness, along with optimal weight selection procedure, has led the proposed model to give better results on simulation and analyzation than preexisting algorithms. [ABSTRACT FROM AUTHOR]

With the development of nuclear energy technology, reactor physical calculations have higher requirements for calculation accuracy and speed, and it has become an inevitable trend to use high-performance computers for reactor simulation calculations. The method of characteristics (MOC) is currently recognized as the preferred method for simulating neutron transport in the nuclear reactor core. Based on the architecture of Sunway many-core processor and Sunway Bluelight II supercomputer, this paper proposes a fine grained and universal two-level parallelization, including thread-level parallelization and process-level parallelization. In the thread-level parallelization, the methods such as job pipeline optimization, load balancing across CPEs, and I/O optimization are proposed for acceleration. In the process-level parallelization, a mapping method from software to hardware is proposed. This method can make full use of the hardware of Sunway supercomputers and improve the computing efficiency and data transmission efficiency. For the first time, the OpenMOC program is transplanted and parallelly optimized on the Sunway supercomputers, which enriched the application ecology of Sunway supercomputers. Compared with the original program, the two-level parallelization can achieve up to 18.6x speedup. Moreover, our parallelization is capable to run on more than 3750 processes of Sunway Bluelight II supercomputer with good strong and weak scalability. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) include a collection of distinct and autonomous nodes that move independently and send data using wireless channels. Clustering and secure routing are treated as proficient techniques to achieve energy efficiency and network stability. Different methods have been presented for addressing the issues of mobility and battery limited characteristics of mobile nodes in MANETs. This paper aims to develop new cluster-based secure routing protocols in MANETs. This paper presents a new Type II fuzzy-based clustering with improved ant colony optimization-based routing (T2FCATR) protocol for secured data transmission in MANETs. Initially, a new type II fuzzy-based clustering process takes place to construct the clusters. Then, the routing issue is addressed by ACO with the Tumbling technique, which offers intercluster routing from CHs to BS. In addition, a hybrid data transmission mechanism is also involved to reduce the number of data transmissions and thereby achieve energy efficiency. The performance of the T2FCATR model has been validated against several aspects. The simulation outcome implied that the T2FCATR model achieves minimum clustering overhead, minimum variations in CHs and CMs. As a whole, it can be concluded that the T2FCATR model is superior to all the compared methods under several aspects. [ABSTRACT FROM AUTHOR]

In recent years, DPDK (Data Plane Development Kit, a data plane development tool set provided by Intel, focusing on high-performance processing of data packets in network applications), one of the high-performance packet I/O frameworks, is widely used to improve the efficiency of data transmission in the cluster. But, the busy polling used in DPDK will not only waste a lot of CPU cycles and cause certain power consumption, but also the high CPU usage will have a great impact on the performance of other applications in the host. Although some technologies, such as DVFS (dynamic voltage and frequency scaling, which is to dynamically adjust the operating frequency and voltage of the chip according to the different needs of the computing power of the application running on the chip, so as to achieve the purpose of energy saving) and LPI (low power idle, a technology that saves power by turning off the power of certain supporting circuits when the CPU core is idle), can reduce power consumption by adjusting CPU voltage and frequency, they can also cause performance degradation in other applications. Using thread sleep technology is a promising method to reduce the CPU usage and power consumption. However, it is challenging because the appropriate thread sleep duration cannot be obtained accurately. In this paper, we propose a model that finds the optimal thread sleep duration to solve the above challenges. From the model, we can balance the thread CPU usage and transmission efficiency to obtain the optimal sleep duration called the transmission performance threshold. Experiments show that the proposed models can significantly reduce the thread CPU usage. Generally, while the communication performance is slightly reduced, the CPU utilization is reduced by about 80%. [ABSTRACT FROM AUTHOR]

Recently, a crucial need has grown for improving data communication for the application of roads and ad hoc networks. That is, to provide reliable and operational efficiency in data delivery and throughput. Due to the fast fragmentation and dynamic network behavior, there is an increasing demand to reach reliability in data transmission. Furthermore, the various features and the manifold of dynamic topologies in the vehicular ad hoc network raise the need to redesign the routing strategy. Thus, ensuring efficient and reliable data delivery. This paper aims to introduce a Software architecture for Road Network. The architecture is based on fog computing and aims to improve the overall performance in vehicular networks. The proposed architecture is a new routing design for the urban system to accomplish low energy consumption and operational efficiency in data delivery. The integration between the software-defined networks and fog computing platform in the proposed architecture aids to address the high rate of data transmission. Historically, this high rate negatively affected network capacity and power consumption. To prove the effectiveness of the proposed architecture, it is compared with five state-of-art algorithms published in high impact journals. The proposed architecture performance is tested based on four metrics namely packet delivery ratio, network throughput, power consumption, and routing overhead. The experimental results indicate that a 50–60% improvement in both power consumption and packet delivery ratio, while a 60–65% enhancement in network throughput and routing overhead, respectively. [ABSTRACT FROM AUTHOR]

Recently, a lot of researches have been conducted on a platform that provides a Visual Programming development environment capable of 3D editing and interactive editing in an In-VR (Virtual Reality) environment so that VR/AR (Augmented Reality) contents can be quickly worked on. In this paper, a functional language-based VR/AR specialized language(VAL) and Visual Language were developed, and VR/AR prototypes were easily implemented using these languages on the platform. Then, prototypes based on the 5G network can be shared and collaborated with others, and content creation can be continued in Unity and Unreal without interruption. In addition, this paper shows the recognition process for users, the PS/CS check process and the node processing process for transmitting content by limiting the process for transmitting VAL-based mass data in 5G environment. The proposed platform can easily create a prototype of their intended VR/AR App (or content) even for users (beginners) who do not have a certain level of knowledge and experience in 3D graphics and software coding, and share it with others. It has characteristics that can be done. Consequently, we propose a method to use VAL more effectively in a 5G environment. [ABSTRACT FROM AUTHOR]

Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the most popular techniques which provide the required energy-efficient communication and scalability in large-scale WSNs. In clustering, WSN is divided into number of clusters, and cluster head is selected in each cluster. However, in the existing clustering protocols, CH's near base station undergoes large number of receiving, aggregating and transmitting operations in comparison with far away CHs. This imbalance of load on CHs and lack of structured multi-level clustering framework leads to early death of WSNs. Moreover, resolving the issues of scalability and data reliability along with load balancing is a very tedious task. In this paper, a hierarchical clustering and routing (HCR) protocol is proposed to formulate a load-balanced approach for clustering while taking care of energy efficiency, reliability and scalability. Firstly, a hierarchical layered framework is created to split the WSN into virtual circular layers for efficient transmission of data in hierarchical fashion. Subsequently, an ant lion optimizer is employed for the selection of CHs to ensure reliable, energy balanced and scalable cluster formation. Simulation results demonstrate that HCR protocol outperforms existing state-of-the-art clustering protocols in terms of network lifetime, balanced clustering, throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

The recent technological advancements such as IoT-enabled sensor nodes, Global Positioning System, Wi-Fi transceivers, and lightweight lithium-ion batteries enable the use of Unmanned Aerial Vehicles (UAV) for data collection in wireless sensor networks. In a UAV-assisted wireless sensor network (UAV-WSN), the sensor nodes are installed at the ground and a UAV works as the sink node. The UAV-based sink flies over the sensed region and receives the data packets of surrounding ground nodes. A UAV-WSN offers improved data collection efficiency as the UAV-based sink avoids the ground obstacles and establishes line-of-sight communication with the ground sensor nodes. However, the UAV's flight trajectory needs to be optimized to achieve minimized UAV energy consumption during flight operation and minimized node energy consumption in data transmission. This paper presents a hybrid data routing protocol for UAV-WSN that considers optimized planning of the UAV's flight trajectory in parallel with energy-efficient data communication amid ground sensor nodes and the UAV. The presented scheme utilizes multi-objective NSGA-II optimization heuristics to optimize UAV's flight trajectory. The developed NSGA-II model evolves into an optimal UAV flight trajectory that simultaneously achieves the objectives of minimized UAV energy consumption, minimized node energy consumption, and maximized average RSSI. A maximized RSSI further brings about a significant increase in network throughput rate. Simulation results depict that the proposed UAV-WSN scheme achieves improved network lifetime and network throughput rate compared to other state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

The traditional variational mode decomposition (VMD) algorithm causes under-decomposition and over-decomposition problems when performing signal decomposition. Many studies have addressed various optimization algorithms. When the optimization parameters are set too small, the best decomposition cannot be found. On the other hand, setting too large parameters leads to a waste of computational resources. Therefore, an improved VMD algorithm is proposed in this paper, which effectively solves the above problems by establishing an adaptive method for choosing the values of decomposition parameters and its iteration termination condition. In order to meet the monitoring data transmission requirement, a hybrid compression scheme based on the improved VMD is proposed. The monitoring signal is effectively decomposed by the improved VMD algorithm, and then thresholded by the lifting wavelet transform (LWT) method, and further compressed by combining quantization as well as entropy encoding methods. It is experimentally verified that its compression scheme can effectively improve compression performance and has a high signal-to-noise ratio, providing an effective solution for data transmission in equipment monitoring. [ABSTRACT FROM AUTHOR]

Multistage Interconnection Networks (MINs) are designed to provide efficient communication via switching. These kinds of MINs are available for large-scale parallel processing. One of such kinds of MINs is Shuffle-Exchange Network (SEN), which facilitates a high level of data transmission with a number of processors working together. Designing this SEN architecture requires a different kind of switching and has wide applications in different kinds of interconnection networks such as omega, cube and binary. Previously, many topologies such as Arrayed Waveguide Gratings and tuneable Wavelength Converters have been presented, but these methods faced low reliability and fewer path possibilities. In these topologies, the connections were made backtracking and non-blocking. These connections are found to be low reliability and with improper control structures. In order to overcome these issues in the design, this paper proposed an enhanced SEN architecture using crossbar switches with five input and output ports that enabled overall path possibilities, thus improving network reliability. Path connections were done using shuffle exchange connections and permutations over source tags that are fault-tolerant. Similarly, a routing table has been implemented to optimise path selection and make the system non-blocking with less switch failure. A tag-based routing has been enabled to generate this routing table, and these tag bits are control lines of the path. There remain three disjoint paths in this routing. Routing has been done using these tags and implemented in enhanced SEN architecture, and the performance has been analysed. [ABSTRACT FROM AUTHOR]

The sink mobility in wireless sensor networks (WSNs) brings new challenges specifically while designing the routing protocols. The advertisement of the mobile sink location to the sensor nodes is one of these challenges that is a core problem for any routing protocol. To deal with this issue, several hierarchical routing protocols have been developed for different mode of data transmissions, i.e., event-driven and query-driven, in mobile sink-based WSNs (MSWSNs). A routing protocol considers a particular data transmission mode depending on the application requirement. Routing protocols designed for event-driven scenarios do not ensure their efficient working in query-driven scenarios and vice versa. Thus, each application requires some specific designing of routing protocol. By considering the impact of data transmission mode on routing protocols, this paper makes the very first attempt to provide a comprehensive survey on hierarchical routing protocols designed for query-driven and event-driven data transmission scenarios. We provide a comparative study by discussing and describing their functionalities along with their advantages, disadvantages and key performance parameters. To help readers understand the evolution within each category, the relationship among different routing protocols is outlined with detailed descriptions as well as in-depth analysis. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the substantial parts of any Internet of Things (IoT)-based applications. A WSN is deployed to collect essential data from a specific application field in the IoT. A WSN can fail for various reasons, which can disrupt the operation of numerous IoT applications. The main threat to network failure is topology changes, node failure due to battery drain, or breakdown of the wireless nodes communication module. An effective fault tolerance mechanism for WSN can guarantee reliable data acquisition and transmission in IoT applications. This paper proposes a fault-tolerant cluster-based routing protocol for WSNs using a hybrid algorithm called FAGWO-H, which combines Firefly Optimization (FA) and Grey Wolf Optimization (GWO). FA is employed for optimal clustering, and GWO chooses the best route between the cluster head (CH) and the base station. The proposed method uses two novel fitness functions for FA and GWO. The algorithm considers the energy efficiency and fault-tolerant aspect of the sensor nodes and CHs to improve network performance while meeting QoS criteria. We assessed the effectiveness of the techniques using several WSN scenarios and compared them to the existing state of art methods. [ABSTRACT FROM AUTHOR]

The network-on-chip is a subsystem based on simplified public data network communication protocols for routing using routers. Providing an efficient solution is not a trivial issue due to communication problems caused by network element failure on the chip. The purpose is to maintain the performance of the router in the presence of failure. Therefore, in this paper, by combining path length and an adaptive congestion-aware approach using multi-criteria decision-making technique, a reliable method is introduced that, in case of failure, replaces a reliable path with similar quality of service features. The throughput increase is confirmed in the simulation results of the proposed solution, up to 54.05% and 8.05% with a two-criteria and a single-criteria router, respectively. The hardware overhead of the algorithm is relatively low cost, which maintains scalability for implementation in large network-on-chips. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a MEC relay-assisted system with multiple relay nodes (RNs) and multiple remote servers (RSs), where both the selections of best RN and RS are considered. In order to explore the diversity of both RNs and RSs, we propose a sequential relay-remote selection and offloading strategy by taking the local computing, relay computing and remote computing into consideration. The sequential relay-remote selection strategy specifies when to stop server discovery and carry out computation offloading. The offloading strategy tells what are the optimal portions of task executed locally, at RN and at RS. Under such framework, we seek to minimize the total energy consumption coming from server detection, data transmission and data execution. We formulate this problem as a stochastic sequential decision-making problem and dynamic programming is applied to obtain the optimal strategy. The performance of our proposed strategy is evaluated using simulation results. It is found that the energy consumption can be reduced by jointly considering the design of sequential relay-remote selection and offloading ratio optimization. [ABSTRACT FROM AUTHOR]

Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55 to 70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique. [ABSTRACT FROM AUTHOR]

Multi-secret sharing (MSS) is an effective technique that securely encodes multiple secrets to generate shares and distributes them among the participants in such a way that these shares can be used later to reconstruct the secrets. MSS schemes have a considerable advantage over the single-secret sharing schemes for secure multimedia data transmission. This paper presents a novel secret image sharing approach, namely '(n, m, l)-Multilevel Multi-Secret Image Sharing (MMSIS) scheme.' The proposed MMSIS scheme encodes 'n' distinct secret images to generate 'm' shares and distributes them among 'm' participants allocated to 'l' distinct levels. The paper proposes two variants of the MMSIS scheme. The first variant is an (n , n + 1 , l) -MMSIS scheme which encodes 'n' secret images each having a unique level id L k into (n + 1) shares. The image shares are then distributed among (n + 1) participants assigned to ' l = n ' different levels. With the increase in level id, the number of shares required to reconstruct the secret image also increases. To reconstruct a secret image of a particular level L k , all the shares at level L k and its preceding levels need to be acquired, which requires the consensus of all participants holding the shares up to level L k . The second variant, namely extended-MMSIS (EMMSIS) scheme is a generalized (n, m, l) version of the former scheme that allows to generate more shares for a specific secret image at a particular level in accordance with the consensus requirements for its reconstruction. The multilevel structure of the scheme makes it useful for multi-secret distribution in a multilevel organizational structure. [ABSTRACT FROM AUTHOR]

OpenMP supports the use of target offloading compile guidance instructions to invoke heterogeneous-platform accelerators to compute core code segments; however, unreasonable use of target offloading instructions can make the data transfer process time-consuming. The problem of unused array transfer and unused data segment transfer arises when the amount of data transferred from the host side to the device side exceeds the amount of data required for the core computation on the device side. For the transmission of unused arrays, the use of the transmitted arrays is guided by adding a filter to eliminate the transmission of redundant data; for the transmission of unused data segments, the use of arrays is quickly determined on the basis of the filter, and valid data are transmitted by optimizing Clang's code generation strategy after obtaining the lengths of the data segments in core computation. Experiments are performed using the Polybench benchmark; the optimized speedup for unused array transfer reaches 7%, and the optimized speedup for unused data segment transfer reaches 10%. The experimental results show that data transfer optimization for target offloading characteristics can help improve program performance. [ABSTRACT FROM AUTHOR]

The maximization of the network lifespan is the main goal of designing SDN-enabled WSNs with limited network resource utilization. Clustering and routing techniques are widely used to balance the network load in SDN-enabled WSNs and achieve energy-efficient and stable network performance. However, one of the critical challenges in clustering is the selection of optimal control nodes (CNs). The CNs selection is challenging due to the high computational complexity during global optimization. Thus, it is necessary to optimize the CNs selection for the routing process with limited network resources. An intelligent and hybrid model of genetic mutation-based whale optimization (GM-WOA) is proposed to optimize the process of CNs selection in heterogeneous SDN-enabled WSN. In the GM-WOA approach, the self-adaptive inertia weights, fitness function and genetic mutation operation are used for dynamic CN selection and energy-efficient data transmission. The genetic mutation approach distributes CNs more evenly throughout the network to balance the network load. The GM-WOA performs at ONOS controller and simulated using ns-3 simulator. The simulation results demonstrate that the proposed nature-inspired GM-WOA approach is superior to existing techniques in terms of various performance metrics. These metrics include stability period, alive nodes per round, packets transmitted to control server, network residual energy, fitness value, average delay, and Jitter [ABSTRACT FROM AUTHOR]

In today's Internet of Things (IoT) era, a wide range of wireless devices communicates together through wireless communication. Wi-Fi has been used for a large number of data transmissions due to its high range, high performance and universal IP accessibility. Wi-Fi is generally power consuming, and it may put a lot of stress on energy-constrained IoT devices and gateways. Conversely, ZigBee has now become a broadly used transmission technique in IoT because of its minimal price, low power consumption and ease of implementation. Many power saving management schemes have been designed to increase energy efficiency, but they do not perform well for all power constraint services. To deal with such situations, we have proposed an Energy-Efficient Scheduling (EES) using Wi-Fi and ZigBee and utilized the high transmission rate of Wi-Fi and low power consumption nature of ZigBee. While working on these two technologies of the same frequency band (2.4 GHz), we have resolved the interference problem using Interference Avoidance (IA) algorithm. Inet framework of Omnet++ simulator is used for the simulation. The implementation result shows a significant reduction in energy consumption on the device and gateway. In the presence of Wi-Fi, ZigBee functions better and the outcomes indicate better throughput while maintaining the energy consumption and interference level. [ABSTRACT FROM AUTHOR]

Decentralized mobile ad hoc networks (MANET) is a self-organizing and adaptive method comprised of multiple mobile nodes. Due to the dynamic and flexible nature of network topology, wireless communication links face major issues based on trust and security. The researchers came to know that MANET suffers greatly from the quality of service (QoS). So, MANET recommends the employment of an intrusion detection system to identify intrusions, namely attacks, message eavesdropping, and untrustworthy routing. Intrusion detection is considered a significant task to enhance network security from unauthorized access. The main issue in MANET is the limited battery source of sensor nodes which causes communication blockage after a certain transmission range. Therefore, to offer continuous data transmission without any intrusion, in this article, we introduced a secure high-yielding carry-out approach "modified k-means Philippine eagle (MKMPE) approach." Initially, for the determination of the ideal cluster head (CH) from the cluster group, the modified k-means algorithm (MKM) employs for communicating information to the destination node without any packet loss. The CH is selected according to the values of credits namely indirect, recent trust, and direct trust. Then, with the support of Philippine eagle (PE) optimization, the energy-efficient and secure paths are discovered to transfer the information with minimum delay. To examine the potential ability of the proposed MKMPE approach, various state-of-the-art methods are compared with the suggested method in terms of evaluation metrics, namely energy consumption, network lifetime, end-to-end delay, latency, packet delivery ratio, throughput, and detection rate. This proposed MKMPE approach achieved a maximum detection rate of about 78%, a throughput rate of 0.86 bps, and minimum latency of 0.09 s concerning a maximum time interval of 45 ms. [ABSTRACT FROM AUTHOR]

To improve the network lifetime of battery-operated wireless sensor networks (WSNs), effective deployment of available low-power resources is required. This is essential as it is impractical to exchange or recharge the energy resource after deployment. To properly utilize the limited energy, a suitable cluster-based data aggregation technique is required, which considerably reduces the network's overall energy usage. Therefore, this work proposes a cluster-based data aggregation technique called Scalable Clustering Algorithm for Data Aggregation (SCADA). Each cluster has a single cluster head (CH) and several cluster relays (CRs) in this algorithm. The number of CRs in a cluster varies depending on the cluster's geographic position in relation to the Base Station. We assure that energy usage is reduced as a result of the multi-hop short-distance data transmission by doing so. In hybrid modes, the CHs are also rotated at the appropriate intervals to reduce control packets. The proposed algorithm is novel and well suited for both homogeneous and heterogeneous WSNs because it uses a hybrid CH selection mechanism, a threshold-based CH rotation mechanism, and a mechanism of allocating dedicated CR in each cluster. Effects of variations in the length of the sensing field, variation in SN density, variation in Cluster count, and variation in initial energy and heterogeneity test are used to assess the proposed algorithm's efficiency. SCADA is incredibly scalable for large-scale WSN and energy-efficient with a small number of control messages as depicted by the analytical results. Simulation-based results, clearly states that as compared to three protocols EESCT, DUCF, and MUCA, SCADA obtains approximately 35%, 25%, and 10%, respectively, more rounds of communication. SCADA drastically improves when the length of the sensing field increases as compared to EESCT, DUCF, and MUCA. SCADA when compared to the EESCT, DUCF, and MUCA algorithms, the average energy consumption is significantly lower by 21 to 38%. [ABSTRACT FROM AUTHOR]

Fog computing enables cloud and edge resource integration. It provides intelligent, decentralized processing of the amount of data generated by the Internet of Things (IoT) sensors for seamless integration of physical and cyber environments. This can create many benefits for society. The IoT framework uses wireless nodes to collect data and monitor the environment. Therefore, the limited power of batteries and the network lifetime are two essential problems in an IoT-based environment. Reducing energy consumption and fast transmission of collected data for reducing the response time can be improved the performance of IoT. Sending data packets by the appropriate header nodes can reduce the communication latency to the Fog. So, in the first step of the proposed method, the header nodes are selected based on many factors including the criteria of residual energy, speed of nodes, and number of neighbors in the Fog-IoT method. In the second phase, we implement a self-adapting algorithm using a control loop to solve the service location problem in the Fog-IoT approach. By considering the feature of self-adapting computing which refers to the self-management features of the distributed computing resources, the proposed method provides an optimal and centralized framework and methodology for solving the problem of deploying the Fog service based on self-adapting computing. Therefore, the real-time application requested and tasks allocation can be dynamically accepted in the shortest possible time in the Fog and the result returns to the IoT-based wireless devices. The simulation of the proposed method under the several performance metrics including the packet delivery ratio, throughput, and network lifetime has been compared with the previous works. The results show that the proposed Fog-IoT method improves all metrics in various scenarios. [ABSTRACT FROM AUTHOR]

A smart grid uses automation and information communication technologies to guarantee its integrity. The first step of smart grid construction is the deployment of an advanced metering infrastructure system, which can read meter data automatically and maximize energy efficiency. Messages are collected at hubs distributed throughout the smart grid and sent through limited network resources. The advanced metering infrastructure system comprises a meter data management system, concentrators, relay stations between a set of smart meters and the control center, and smart meters that can collect the information of thousands of users. Concerns regarding this system are the loading on the control center, the volume of data traffic, and the quantity of the data. This study analyzes various situations of data traffic, uses Poisson process, and proposed two schemes to reduce the transmission frequency and the total data volume. A concentrator is determined to reduce the transmission frequency and compress the data from smart meters to the control center, thereby reducing the total data volume. Furthermore, the proposed scheme can reduce the loading on the control center. [ABSTRACT FROM AUTHOR]

Energy- and spectrum-efficient communication in massive multiple-input multiple-output and multiple-input single-output (MISO)-based heterogeneous network and select the leader small cell using Stackelberg game are proposed in this article. Intersection areas of MISO small cells mobile devices are able to receive the signal from both small cells. Among the small cells, we select the leader small cell using Stackelberg game with two proposed utility functions, for data transmission. We have proposed two algorithms for selecting the leader small cell and frequency allocation between leader and follower small cells in this article. We have analyzed the spectrum of the signal through Vector Signal Generator and Vector Signal Analyzer. The power consumption, SINR and spectral efficiency of the proposed E 2 M 3 are calculated. Power consumption of the proposed E 2 M 3 decreases by ∼ 30%, SINR and spectral efficiency increases by ∼ 20% and ∼ 6%, respectively, than existing approaches. [ABSTRACT FROM AUTHOR]

In massive IoT (Internet of Things) environment, which constitutes a goal of the 5G standard, the communication channel of an MTC (machine-type-communication) device with narrowband transmission has problems that are deep fading and interference with the transmission performance. For improved communications in IoT environment, researchers aimed to increase the transmission throughput by using larger contention windows between channel hops when the probability of successful data communication drops during the previous time step to prevent the rapid increase in the number of connected devices. Hence, researchers propose a model to change the interframe spacing according to the number of contention windows for hopping and data prioritization by quickly determining network traffic variations in a massive network environment. Specifically, researchers propose CSMA/CA (carrier-sense multiple access with collision avoidance) channel hopping to prevent deep fading and obtain multiuser diversity in multichannel environments as well as improve the channel quality. Researchers confirmed that the proposed CSMA/CA channel hopping provides a data transfer rate about twice that of conventional TDMA (time-division multiple access) channel hopping for 60 connected devices. As a result of analyzing data transfer rates in channel hopping, it was shown that the collision probability is not high when the number of nodes is small, so the number of channels and contention window size are not significantly affected, but as the number of nodes increases, the number of channels and contention window size are greatly affected. [ABSTRACT FROM AUTHOR]