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This paper presents the implementation and experimental evaluation of a short-packet physical-layer network coding (PNC) system. Implementation of short-packet PNC systems is challenging. First, short packets may have only a few pilot symbols for synchronization and channel estimation purposes. Increasing the number of pilots increases the overhead; decreasing the number of pilots, on the other hand, degrades the packet error rate performance. Second, many short-packet systems are meant for applications with very stringent delay requirements. Employing advanced but complex PNC channel decoding may result in unacceptable delay due to the processing delay. This work presents a low-complexity and low-overhead physical-layer design of OFDM-based short-packet PNC systems, implemented over the software-defined radio platform. Our design makes use of only a small number of pilots (without separate OFDM preamble symbols) to address issues such as slot synchronization, packet detection, carrier frequency offsets, and mismatched channel state information. Our design employs reduced-complexity XOR channel decoding based code-aided parameter estimation (that includes synchronization and channel estimation) to compensate for the limitations imposed by having a small number of pilots. This is the first demonstration that provides a practical framework for applying PNC to short-packet communications.

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two- way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this paper, we propose a privacy-preserving fair exchange scheme V2GEx for V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions cost only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. In this paper, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22\% to 41\% compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this paper, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

With the explosive growth of global data volume, the centralized cloud computing computing model cannot provide low-latency, high-efficiency video surveillance services. Therefore, a distributed computing model is proposed, which directly processes peripheral video data to reduce transmission pressure, the burden of the central cloud server and the processing delay of the video surveillance system. Combined with the federated learning algorithm, a light neural network model is proposed. The light neural network is used to generate calculation models in different scenarios, and the generated models are reasonably arranged in edge devices. With the continuous development of technology, human motion analysis has become a hot research field in recent years, and human motion recognition is developed on the basis of human motion analysis. With the development of society, the level of Chinese Taekwondo has improved compared to before. Therefore, Chinese Taekwondo classes have gradually developed, but behind this phenomenon there is a problem of uneven teaching quality. In order to help solve the teaching problems in the learning process of Taekwondo and further improve the level of Chinese Taekwondo, the author carried out specific research in conjunction with D.A. Cooper's empirical learning theory. Future work will continue to study federated learning algorithms, optimize general neural network weight update methods, further improve detection results, and introduce neural network compression acceleration algorithms to make the system meet real-time requirements. This research uses scientific and technological means as a guide to study technical actions and strategies, and apply these strategies to specific teaching experiments for testing.

In Wireless Sensor Networks (WSNs), data aggregation (DA) has become one of the most practical techniques to reduce processing delay and improve energy efficiency. To support intelligent applications, sensor nodes need to report heterogeneous and diverse data, which induces the demand for multi-dimensional data aggregation and multi-functional data analysis. To solve the current security problems and functional requirements, we propose a multi-functional and multi-dimensional secure data aggregation scheme to strike the balance between data availability and privacy. Firstly, we design a Chinese remainder theorem conversion method with the counter to encode multi-dimensional data into large integers, which can be operated by linear homomorphic encryption schemes. Then, we introduce a multi-functional data analysis method supporting diversified aggregation functions, including linear, polynomial and continuous functions. Moreover, we demonstrate that the proposed scheme can achieve confidentiality, integrity, authentication and resistance against false data injection attacks. Experimental results show that the supported max dimension of one ciphertext in our scheme is at least twice that of existing schemes. Thus, in scenarios with high dimensions, our scheme is superior to the existing schemes in terms of computation and communication costs.

Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes.

Traffic redundancy elimination (TRE) is a key solution for saving networking resources by removing redundant traffic transfers in networks. However, current TRE systems mainly rely on end-side software programs at the cost of packet processing performance due to their complex computational requirements. This makes it difficult to deploy the TRE technique for various networking scenarios in practice. This study proposes TRE-PRO, a TRE system over a programmable data plane (PDP) paradigm. PDP provides flexible and high-performance packet processing that enables the implementation of on-path deployable and high-performance TRE systems. This article explores the design and implementation of TRE-PRO in the state-of-the-art programmable switch architecture. It is shown that TRE-PRO overcomes the shortcomings of existing TRE systems, such as packet processing delay. The performance evaluation results show that TRE-PRO can eliminate redundant traffic at the modern programmable switch without compromising any processing delay.

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

In modern decentralized Internet-of-Things (IoT)-based sensor communications, pseudonoise-diffusion oracles are heavily investigated as random oracles for data exchange among peer nodes. As these oracles are generated through algorithmic processes, they pass the standard random tests for finite and bounded intervals only. This ensures a false sense of privacy and confidentiality in exchange through open protocol IoT-stacks in public channels, i.e., Internet. Recently, blockchain (BC)-envisioned random sequences as input oracles are proposed about financial applications, and windfall games like roulette, poker, and lottery. These random inputs exhibit fairness, and nondeterminism in SC executions termed as probabilistic smart contracts (PSCs). However, the IoT-enabled PSC process might be controlled and forged through humans, machines, and bot-nodes through physical and computational methods. Moreover, dishonest entities like contract owners, players, and miners can co-ordinate together to form collusion attacks during consensus to propagate false updates, which ensures forged block additions by miners in BC. Motivated by these facts, in this article, we propose a BC-envisioned IoT-enabled PSC scheme, SaNkhyA , which is executed in three phases. In the first phase, the scheme eliminates colluding dishonest miners through the proposed miner selection algorithm. Then, in the second phase, the elected miners agree through the proposed consensus protocol to generate a stream of random bits. In the third phase, the generated random bit-stream is split through random splitters and fed as input oracles to the proposed PSC among participating entities. In simulation, the scheme ensures a trust probability of 0.38 even at 85% collusion among miners and has an average block processing delay of 1.3 s compared to serial approaches, where the block processing delay is 5.6 s, thereby exhibiting improved scalability. The overall computation and communication cost is 28.48 ms, and 101 bytes, respectively, that indicates the efficacy of the proposed scheme compared to the traditional schemes.

Nowadays, there is a great demand for ultra-high data rate (UHDR) transmission on most 5th generation wireless networks. In this concern, the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is used on a large scale to achieve UHDR transmission with reduced inter-symbol interference (ISI) and inter-carrier interference (ICI). Discrete wavelet transform-based OFDM (DWT-OFDM) provides better orthogonality due to presence of orthogonal wavelets, which mitigates the effects caused by ISI and ICI. Also, it has extended bandwidth than the traditional OFDM systems. But a major drawback in this system is that it suffers from down sampling. The down-sampling effect reduces the actual size of the input bit streams. As a result, the system performance is degraded. For solving this problem, a multilevel redundant discrete wavelet transform (ML-RDWT) is used instead of DWT to achieve improved spectral performance. Here, complex down-sampling operation is eliminated. From the simulation outcomes, it is clearly viewed that effects caused by ICI, ISI and BER are mitigated by improving the performance of CIR. The proposed method employs optimal red deer algorithm (ORDA) to locate the optimized weights for the ICI cancellation system. This algorithm enhances the spectral efficiency by achieving high CIR with reduced BER, ISI and ICI. The BER in the proposed MIMO-ML-RDWT-OFDM-ORDA method is 68%, 76%, 38% and 75%, which is very low when compared to the BER in the existing techniques like MIMO-DWT-OFDM-RDA, MIMO-RNS-OFDM-PNMA, MIMO-OFDM-BMA and MIMO-OFDM-ICIMA. The ISI in the proposed method is 94%, 91%, 95% low when compared to the ISI in the existing techniques. The ICI in the proposed work is 71%, 57%, 73% and 86% low when compared to the ICI in the existing techniques. Therefore, the general performance of the proposed MIMO-ML-RDWT-OFDM-ORDA method is improved in an efficient way with less complexity, error rate and processing delay.

This paper assesses the relative performance of the MQTT protocol in comparison to the Secure Remote Update Protocol (SRUP) in a number of simulated real-world conditions, and describes an experiment that has been conducted to measure the processing delay associated with the use of the more secure protocol. Experimental measurements for power consumption of the devices and the size of comparable TCP packets were also made. Analysis shows that the use of the SRUP protocol added an additional processing delay of between 42.92 ms and 51.60 ms—depending on the specific hardware in use. There was also shown to be a 55.47% increase in power consumption when running the secure SRUP protocol, compared with an MQTT implementation.

This letter presents our initial results for real-time orthogonal frequency division multiplexing (OFDM) signal modulation classification based on deep learning and software-defined radio. We generate a modulation classification dataset under a dynamic fading channel, including 6 different OFDM modulation signals, and propose a novel neural network with triple-skip residual stack (TRS) as the basic unit. Each TRS has multiple residual units with gradually increasing convolutional layers. Finally, a near real-time classification system is designed based on the proposed network and GNU Radio. The processing delay incurred by the detection and modulation classification is about 4 ms. It is worth mentioning that the classification accuracy can reach 64% at −10 dB, which is about 7% higher than ResNet and VGG.

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory . Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

We consider distributed computation of a sequence of $J$ gradients $\{\mathbf {g}(0), \ldots,\mathbf {g}(J-1)\}$ . Each worker node computes a fraction of $\mathbf {g}(t)$ in round- $t$ and attempts to communicate the result to a master. Master is required to obtain the full gradient $\mathbf {g}(t)$ by the end of round- $(t+T)$ . The goal here is to finish all the $J$ gradient computations, keeping the cumulative processing time as short as possible. Delayed availability of results from individual workers causes bottlenecks in this setting. These delays can be due to factors such as processing delay of workers and packet losses. Gradient coding (GC) framework introduced by Tandon et al. uses coding theoretic techniques to mitigate the effect of delayed responses from workers. In this paper, we primarily target mitigating communication-level delays. In contrast to the classical GC approach which performs coding only across workers ( $T=0$ ), the proposed sequential gradient coding framework is more general, as it allows for coding across workers as well as time. We present a new sequential gradient coding scheme which offers improved resiliency against communication-level delays compared to the GC scheme, without increasing computational load. Our experimental results establish performance improvement offered by the new coding scheme.

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier’s quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel’s frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

IoT systems make a significant contribution to the growth of global traffic. There is a trend to reduce the volume of transmitted and stored data, for which different approaches are used. Most approaches use traditional data processing in the Cloud and at the gate level. The end devices are not involved to the data processing. The article describes the correlation method of data processing on the end device. The results of a research of the maximum performance when implemented on an FPGA, with different number of taps of the matched filter and different bit width of the input data stream are presented. This method allows to work with high-speed data streams and communication channels typical for IoT systems. The boundary conditions of operability for the correlation method are given. On the basis of the presented model of the IoT system, data processing delays for the correlation method and the method using data processing on the gate application of IoT devices for fast data processing are analyzed.

Heterogeneous energy and traffic-aware (HETA), an efficient traffic scheduling algorithm is proposed for forecasting the data traffic and the time-sensitive data process. In this structure, the time of non-intermittent critical traffic delays and the time slots are allocated for the intermittent data traffic. Also, the central sleep scheduling algorithm is used to offer an efficient time scheduling mechanism for the priority and time intermittent data towards the central Base Station. In this paper, the queuing delay against the traffic near the Base Station and the processing delay has been considered. The analysis of end-to-end delay by performing well in simulation study through improved efficiency in the performance of the HETA algorithm 90% more in lifetime of the Network with consistent of 3.7 times increase in throughput.

This paper proposes a novel convolutional neural network (CNN) based joint classification method to characterize the signal-to-noise power ratio (SNR) and Doppler shift using spectrogram images, in order to enable efficient adaptive modulation and coding (AMC) designs. It is necessary to maintain high communication performances even in stringent environments where transceivers move at high speed due to the diversification of wireless applications. To optimize the transmission rate in such dynamic environments, AMC scheme is known to be effective. AMC is generally designed based on feedback information (FBI) such as the SNR and Doppler shift acquired on the receiving side. Here, the challenge is an increase in calculation burden, processing delay and estimation accuracy of the FBI. We focused on the spectrogram which is composed of power values in the time and frequency domains. Its two-dimensional fluctuation represents the Doppler shift as well as noise values. In the proposed method, such key information for AMC can be simultaneously extracted from a single spectrogram via a trained CNN. Therefore it is expected to contribute to reducing the computational burden and speeding up the signal processing. Simulation results are presented to demonstrate that the proposed method achieves better performance than traditional methods.