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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]

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]

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 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]

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 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]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

The security and privacy of data are major concerns in the mobile crowdsensing (MCS) environment due to the huge amount of heterogeneous data received from various users and devices automatically or manually regarding their surrounding environment. User participation in the MCS approach is highly essential to have a vast dataset for analysis that will provide the required information or beneficial solution for society. However, it is difficult to achieve due to huge energy consumption, the need for internet connectivity for data transmission, and the security and privacy of data. Therefore, it is essential to have a network coverage model in which data transmission can be done with minimal energy consumption and the need for internet connectivity can be removed from the user's side. The user's sensitive data needs to be protected from internal and external attackers to improve the efficiency of the solution provided by the MCS environment with genuine data. This work is based on data collection from users based on their experience for a certain location using the hybrid network coverage model based on clustering, in which each location may have just one or multiple heterogeneous cluster heads. Discrete event-based CrowdSenSim Simulator has been used to design a simulation environment in urban spaces in which 2000 users will move to any location randomly among considered 40 locations and provide feedback data for the location. In this paper, a novel security mechanism based on multiple heterogeneous cluster heads per location has been presented, and it provides better security against attackers than the security model with one cluster head per location. The proposed multiple-cluster heads per location (MCHL)-based mechanism has been compared with the vulnerable one-cluster head per location (OCHL)-based mechanism on the basis of the average number of rounds attackers attacked, average number of locations attackers attacked, average coverage and average efficiency of attackers, and average efficiency of system security. [ABSTRACT FROM AUTHOR]

The existing I2C interfaces require significant CPU intervention for data communication. In SOC systems, when using internally integrated I2C modules as masters, software control of I/O ports is necessary to emulate the I2C protocol for data transmission. This paper proposes a Mini I2C bus interface circuit design scheme that supports both master and slave modes. The I2C interface features minimal CPU intervention during data transmission, ease of use, small circuit area, and low power consumption. Additionally, the internal state machine design employs independent finite state machines (FSMs) for master and slave modes, enabling flexible configuration of the I2C module to operate in either mode. In comparison to reference (ShenZhen in Microelectronics Technology CO.BJ8M306A, Datasheet.2019.12.2., 2019), the proposed I2C solution reduces CPU instructions by 50% during data transmission, and by 33% compared to reference (GigaDevice Semiconductor Inc. GD32F1x0, Datasheet, 2022). After DC synthesis, the proposed design occupies only 14% of the area and consumes only 3.6% of the power of the Open Source I2C Design (Forencich in verilog-i2c. GitHub repository. Retrieved from https://github.com/alexforencich/verilog-i2c, n.d.). Therefore, this design scheme is better suited for low-power systems. The proposed design was validated through simulation using Xilinx ISE 14.7 with a SPARTAN 3 FPGA model xc3s500e-5pq208, and finally implemented using Huahong 95 nm CMOS technology, demonstrating high integration and low power consumption. [ABSTRACT FROM AUTHOR]

Ongoing research is focused on underwater wireless sensor networks (UWSNs), which hold great promise for various applications to improve human lives. Many sensor nodes have been strategically positioned in rivers and oceans to closely monitor and analyze the underwater ecosystem. UWSNs face significant challenges due to energy limitations, unreliable communication channels, hole occurrence, and low packet delivery ratio. This paper introduces effective energy routing, hole detection and mitigation, and reliable delivery schemes to address these issues. In ERR-UWSN, a single-path routing strategy optimizes energy consumption by selecting the best-sending node through a unique weight function. However, the reliability of this approach is compromised in the harsh and unpredictable submarine environment. To overcome this limitation, co-ERR-UWSN combines cooperative routing with relay nodes between source–destination pairs. A carefully selected weight function ensures low energy consumption, reliability, and optimal node selection for data transmission. The simulation results show that ERR-UWSN is better than CEER, Co-EPRR, and FB-DBR in terms of residual energy, end-to-end delay, energy consumption, and total number of alive nodes. Also, Co-ERR-UWSN has a special advantage in the PDR criterion compared to other schemes. [ABSTRACT FROM AUTHOR]

With the rapid advancement of internet technology, the demand for network performance continues to grow. Due to the problems of low fault tolerance and limited available bandwidth of traditional single-path transmission methods, multipath transmission has received widespread attention. However, existing multipath transmission schemes may encounter problems such as packet disorder and mutual interference between paths when facing complex network environments, thus reducing network transmission efficiency. This paper proposes a multipath reliable transmission scheme (FC-MPRT) based on fountain codes and recurrent neural networks (RNNs) to address these challenges. The scheme fully leverages the randomness and reliability of fountain codes to effectively solve the buffer-blocking problem caused by frequent packet loss retransmissions and the requirement for packets to arrive in order. Additionally, a supervised prediction scheme based on RNN is designed at the data receiving end to mitigate the potential resource wastage problem during encoding and decoding. The transmission efficiency is significantly enhanced by predicting the arrival of packets. We evaluate the performance of FC-MPRT in various network scenarios on NS-3 and Mininet platforms. Simulation results demonstrate that FC-MPRT achieves a 66.1% improvement in throughput and a 47.6% reduction in the average block transmission delay compared to existing multipath transmission schemes. [ABSTRACT FROM AUTHOR]

In this modern era, the security and safety of financial and personal digital data are crucial considerations. Blockchain technology offers a secure framework for the transmission of data between devices, without the interference of malicious actors. With the growing demand for this technology, it has become essential to evaluate the impact of its processes on system characteristics to forecast transactions behavior and ensure necessary quality indicators, as well as the stability of system components while the blockchain technology is in operation. Queueing models are a highly effective mathematical tool for analyzing and optimizing the performance of blockchain systems. In this study, a more generic queueing model is utilized to evaluate a blockchain application. The transactions arrive in batches according to a Markovian arrival process, and the service times for the block-generation (the first stage) and blockchain-building (the second stage) follow phase-type distributions. Using matrix-analytic method, we study the model in steady-state. The stationary distribution and the stability condition of the system have been derived. Further, by determining key system performance measures, illustrative numerical examples covering a wide range of batch size and service time distributions have been discussed. In conclusion, our hope is that the methodology and findings presented in this paper will pave the way for the queueing analysis of more extensive and diverse blockchain systems in practice. Furthermore, we believe that it will inspire a series of promising future quantitative and qualitative studies aimed at the development of blockchain technologies. [ABSTRACT FROM AUTHOR]

With the advent of internet-enabled and hybrid technologies, information is becoming increasingly accessible to the general public. Smartphones and other gadgets are used extensively by people to share and promote ideas, in a variety of ways. Human interaction and communication has become more reliable and effective through advanced computing technologies. Quantum computing is an emerging paradigm that will change the lives of individuals and the operations of organizations. Quantum computers solve problems at high speed by operating in a superposition state in which the state can be either zero or one at the same instant. Quantum sensors can be used efficiently in technological research to make accurate measurements and collect data that provide new insights into the behavior of nanomaterials. The use of quantum computing could also speed up the manufacturing process of devices with remarkable properties such as superconductivity, high strength or improved signal performance. Quantum computing has the ability to dramatically speed up the development process of various organizations and increase their efficiency and effectiveness. The security and reliability of data and communication is improved by quantum computing techniques such as key generation and entanglement dispersion. Companies use cryptographic algorithms to protect their data. However, with the advent of quantum computing, cryptographic methods that rely on numerical aspects are no longer sufficient to protect data. Quantum computing is an emerging field that is being applied to various problems that previously could not be solved using conventional methods. Quantum computing plays an important role in the field of information processing, where information is precisely analyzed. Various quantum technologies and algorithms are used to secure company data. This paper provides a systematic review of the literature on the principles of quantum computing. The SLR focuses on achieving four aims "identifying a variety of quantum IoT devices, analyzing their importance in different industries, highlighting the challenges of quantum technology, and presenting various techniques used by researchers to overcome different problems". Quantum cryptography is identified as a key strategy for improving the security of IoT systems and ensuring the security and consistency of information. [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]

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]

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]

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 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]

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]

The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested for computation of all vector pairs: tuning of a GPU kernel with consideration of memory coalescing and using shared memory, minimization of GPU memory allocation costs, optimization of CPU–GPU communication in terms of size of data sent, overlapping CPU–GPU communication and kernel execution, concurrent kernel execution, determination of best sizes for data batches processed on CPUs and GPUs along with best GPU grid sizes. It is shown that all codes scale in hybrid environments with various relative performances of compute devices, even for a case when comparisons of various vector pairs take various amounts of time. Tests were performed on two high-performance hybrid systems with: 2 x Intel Xeon E5-2640 CPU + 2 x NVIDIA Tesla K20m and latest generation 2 x Intel Xeon CPU E5-2620 v4 + NVIDIA’s Pascal generation GTX 1070 cards. Results demonstrate expected improvements and beneficial optimizations important for users incorporating such types of computations into their parallel codes run on similar systems. [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]

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]

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]

Internet of Things (IoT) is a technology in which for any object the ability to send data via communications networks is provided. Ensuring the security of Internet services and applications is an important factor in attracting users to use this platform. In the other words, if people are unable to trust that the equipment and information will be reasonably safe against damage, abuse and the other security threats, this lack of trust leads to a reduction in the use of IoT-based applications. Recently, Tewari and Gupta (J Supercomput 1-18, 2016) have proposed an ultralightweight RFID authentication protocol to provide desired security for objects in IoT. In this paper, we consider the security of the proposed protocol and present a passive secret disclosure attack against it. The success probability of the attack is '1' while the complexity of the attack is only eavesdropping one session of the protocol. The presented attack has negligible complexity. We verify the correctness of the presented attack by simulation. [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]

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]

Energy optimization has become an emerging concern for cloud service providers. Existing methods focus on reducing the energy consumption of either server inside the data center or data transmission between data centers. Moreover, most of the works are based on assumptions that servers and workloads are homogeneous. This is not in accordance with the fact that modern data centers are built from various classes of servers. In this paper, we consider the joint energy optimization of intra- and inter-data center in both homo- and heterogeneous cases. We first propose an optimization model to minimize the joint energy cost of servers and network sides. To tackle the time-coupling constraint of carbon emission, we apply the Lyapunov optimization framework to transform the original problem into a well-studied queue stability problem. For better scalability of time complexity, we derive a distributed solution by using generalized benders decomposition. Then, we extend the model to deal with the situation where requests and data centers are heterogeneous as data centers are typically built from servers with different specifications. To better deal with the dynamic of the network (e.g., the occurrence of faults), we leverage a deep Q-network (DQN) and propose a fault-tolerant DQN-based solution. Finally, the simulation results show the high efficiency of our proposal in cost-saving and performance-enhancing. [ABSTRACT FROM AUTHOR]

The model highly parallel graphics processor systems rely on high-bandwidth DRAM I/O interfaces for large volume of data access at the cost of a lot of energy consumption. In order to reduce the energy consumption of data transmission, modern DRAM uses an asymmetric pseudo-drain I/O interface, which increases the ratio of the termination energy to the transmission energy of logic value 1 in data transactions. This paper proposes the X + B Dynamic Bus Encoding Mechanism, which dynamically selects the encoding mechanism to reduce the number of logical values 1 within different data elements, thereby reducing the data transmission energy consumption of the modern DRAM data bus. The simulation evaluation shows the X + B Dynamic Bus Encoding Mechanism reduces the number of logical values 1 by 38.6%, which saves 31.4% of transmission energy consumption compared to the current bus coding technology. [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]

In this paper, an algorithm is proposed to optimize the network connectivity efficiency of a network with nodes of different energy harvesting rates by using the fewest RNs while ensuring a high success rate of data transmission. The algorithm calculates the weight of each node based on the energy harvesting capacity and then uses it to calculate the edge weight. Next, based on the edge weight, the Kruskal algorithm is used to create a minimum spanning tree (MST). Finally, the quantity of non-leaf nodes of the MST is inspected to verify that it meets the transmission requirements for data flow. If not, such nodes will be deemed as nodes with a low energy capacity. The support of RNs is required for these nodes to guarantee network connectivity. As shown by experimental data, the algorithm can be used to maintain network connectivity with the fewest RNs, which reduces the cost and increases the transmission success rate of data packages. [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]

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]

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]

Efficient compression and reliable data transmission are the major areas in the information theory that governs various applications in mobile communication, Internets and modern digital technology. The exploitation of data patterns and redundancies offers better compression without loss of user information. The trade-off between the storage and the quality is the major requirement to apply the coding scheme in very large-scale integration. Alternatively, the reliable transmission in the presence of noise requires the prior addition of redundancy to the data. Polar codes are the practical codes suitable for channel coding and optimal performance achievement under lossy compression and complexity-based communication environment. This paper focuses on the novel architecture of successive cancellation (SC)-based polar decoding for an effective communication. The coefficient weight computation during the register update consumes a number of components in traditional SC decoding architectures. This paper proposes the semi-folded parallel successive cancellation (SFPSC) algorithm that modifies the coefficient weight computation process for register update resulting in a 4-folded polar decoding architecture to retrieve the information from the transmitted symbols with reduced resource utilization. The performance characteristics of SFPSC algorithm based on FPGA implementation are presented. The decoder architecture based on SFPSC algorithm achieves an efficient resource utilization. The reduction in a number of components efficiently reduces the time consumption. The comparative analysis between the SFPSC-based decoder with the existing SC schemes regarding the number of look up tables, FFs and memory assures the effectiveness of SFPSC in resource utilization. [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]

To mitigate the interference in co-located wireless body area networks (WBANs), this paper proposes an inter-WBAN priority-based capacity allocation scheme based on the Nash bargaining game, and an intra-WBAN priority-based power control scheme based on the Stackelberg game. Moreover, under the network capacity imposed by the Nash bargaining solution, two pricing mechanisms: non-uniform pricing and uniform pricing, are introduced in the Stackelberg game and the Stackelberg equilibrium under each mechanism is achieved analytically. Additionally, owing to the special features of WBANs, the players priorities indicated by exigency of the sensed data and the energy consumption of sensors are considered in the design of utility functions. Extensive simulations show that the proposed schemes are energy efficient and can improve the network quality of service in terms of real time and reliability of critical data transmission. [ABSTRACT FROM AUTHOR]

An opportunistic network is a wireless ad hoc network that has frequently intermittent connectivity due to nodes’ mobility. These spare, unpredictable networks aim to solve the prolonged delay paths by a store-carry-forward scheme. It is difficult to determine the appropriate nodes to forward the messages since there are few opportunistic contacts. The nodes might decide based on the obtained data of the network as a guide to reach a destination. The aforementioned technique is not helpful in case the rate of change in the network topology is higher than the rate of data gathering due to usage restrictions and uncertain available information/knowledge of the future contacts. In this paper, to cope with the challenges imposed by the un-deterministic environment, a risk assessment strategy is considered to evaluate the short and long-term impact of each decision to find the optimal node/paths. This routing scheme can take advantage of the unanticipated connection to make the routing more flexible in short time and with less buffer usage. Our proposed risk assessment algorithms that are based on MALP are mixed with the knowledge about buffer management and network capacity. The prioritized messages to be disseminated as well as better decision to take a risk are the main contributions of this paper. Moreover, the self-adaptive threshold, distributed PID controller, is considered to tackle different threshold levels for each node individually. Numerical results prove that the proposed method drastically increases the delivery ratio and also minimizes the risk of exceeding resource consumption and packet loss. [ABSTRACT FROM AUTHOR]

Todays, XML as a de facto standard is used to broadcast data over mobile wireless networks. In these networks, mobile clients send their XML queries over a wireless broadcast channel and recieve their desired XML data from the channel. However, downloading the whole XML data by a mobile device is a challenge since the mobile devices used by clients are small battery powered devices with limited resources. To meet this challenge, the XML data should be indexed in such a way that the desired XML data can be found easily and only such data can be downloaded instead of the whole XML data by the mobile clients. Several indexing methods are proposed to selectively access the XML data over an XML stream. However, the existing indexing methods cause an increase in the size of XML stream by including some extra information over the XML stream. In this paper, a new XML stream structure is proposed to disseminate the XML data over a broadcast channel by grouping and summarizing the structural information of XML nodes. By summarizing such information, the size of XML stream can be reduced and therefore, the latency of retrieving the desired XML data over a wirless broadcast channel can be reduced. The proposed XML stream structure also contains indexes in order to skip from the irrelevant parts over the XML stream. It therefore can reduce the energy consumption of mobile devices in downloading the results of XML queries. In addition, our proposed XML stream structure can process different types of XML queries and experimental results showed that it improves the performace of XML query processing over the XML data stream compared to the existing research works in terms of access and tuning times. [ABSTRACT FROM AUTHOR]

Data aggregation is an effective mechanism to prolong lifetime in the wireless sensor networks by preventing extra data transmission. However, it may have some adverse effects on accuracy, confidentiality, delay, and privacy preserving. Sensing nodes tends to trade-off between energy demand and security issue. Data can be filtered by temporal correlation of sensory data, and thus, network traffic is controlled. Furthermore, data transmission via unnecessary nodes can be controlled by spatial correlation of sensing devices. This, in turn, prolongs network lifetime. CBDA algorithm was proposed by Hu et al. as a technique based on tree and chain formation and slice-mixing. However, we showed the proposed scheme was vulnerable to stolen key set attack and failed to provide forward secrecy. This has been fixed in proposed key distribution and agreement scheme. In this paper, we propose a continuous hybrid and energy-efficient secure data aggregation (CHESDA) algorithm that trades off between privacy preserving, data integrity, communication overload, delay and accuracy and then chooses the best scenario based on application and importance of the parameters. Here, privacy preserving is maintained by slice-mixing technique. Fuzzy logic is applied to choose optimal slicing in each subtree, and GNY logic is utilized to verify key authentication scheme. Results of analysis and simulations indicate that CHESDA, compared to PECDA and SMART, is more energy-efficient and highly secure with lower communication overhead. [ABSTRACT FROM AUTHOR]

By increasing the internet of things (IoT) applications, multi-channel wireless sensor networks (WSNs) are widely used as a promising technology. Due to power constraints in WSNs, increasing the network's lifetime is a challenging issue that must be satisfied in any design, obviously medium-access control (MAC) and routing protocols. In this paper, we proposed a joint MAC and routing mechanism that improves power consumption as well as both end-to-end delay and packet delivery ratio. For the proposed MAC protocol, adaptive sleep–wake scheduling based on nodes' utilization and energy level helps a sender to avoid multiple synchronization and competition periods for any data transmission. In addition, by prioritizing channels based on the number of competing nodes and channels' utilization, the proposed method prevents channels' saturation. Thus, network load will be balanced over different channels. Furthermore, the opportunistic routing is restricted by a forwarding zone to increase the route quality, which balances loads among different next-hop candidates. Simulation results show the performance improvement of the proposed method over the most similar works (MORR protocol and SLOR protocol). [ABSTRACT FROM AUTHOR]

An efficient integration of Internet of Things (IoT) and cloud computing techniques accelerates the evolution of next-generation smart environments (e.g., smart homes, buildings, cities). The advanced modern cloud networking architecture also helps to efficiently host, manage and optimize the IoT services in smart environments. In this paper, we have considered an "IoT-Cloud" environment where servers are composed of Field Programmable Gate Arrays (FPGAs) which are reconfigurable in nature. The energy consumption is considered as a major driving factor for the operational cost of the "IoT-Cloud" platform. We have proposed an "energy-aware application management" strategy for FPGA-based IoT-Cloud environments, which can efficiently handle sensors' data transmission by positioning them into the best possible coordinates and execute the Service Requests requested by the users. We have compared our strategy performances with an existing technique and the results show that our proposed strategy is capable to achieve high resource utilization with low energy consumption over different simulation scenarios. [ABSTRACT FROM AUTHOR]

Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN. [ABSTRACT FROM AUTHOR]

This paper presents the design of a novel, real-time, wireless, multisensory, smart surveillance system with 3D-HEVC features. The proposed high-level system architecture of the surveillance system is analyzed. The advantages of HEVC encoding are presented. Methods for synchronization between multiple streams are presented. Available wireless standards are presented and compared. A network-adaptive transmission protocol for a reliable, real-time, multisensory surveillance system is proposed. Adaptive packet frame grouping (APFG) and adaptive quantization are deployed to maximize the quality-of-experience (QoE). Measurements of the proposed protocol have been shown to provide superior results compared to existing transport protocols. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energy-efficient routing (PriNergy) method is proposed. The method is based on the routing protocol for low-power and lossy network (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely the quality of service RPL (QRPL). [ABSTRACT FROM AUTHOR]