Spatial-Temporal Graph Neural Networks (STGNNs) have been widely utilized in multivariate time series forecasting (MTSF), but they rely on the assumption of data completeness. In practice, due to factors such as natural disaster, STGNNs frequently encounter the challenge of missing data resulting from numerous malfunctioning data collectors. In this case, on the one hand, due to the presence of missing values, STGNNs easily generate incorrect spatial correlations, leading to the performance degradation. On the other hand, STGNNs require separate training of models for different missing rates, limiting their robustness. To address these challenges, we first propose two important components (interpolation attention and adaptive graph convolution), which utilize normal values to recover missing values into reliable representations and reconstruct spatial correlations. Then, we replace the fully connected layers in simple recursive units with these two components and propose Graph Interpolation Attention Recursive Network (GinAR), aiming to recursively correct spatial correlations and achieve end-to-end MTSF with missing values. Finally, we use data with different missing rates as positive and negative data pairs. By employing contrastive learning to train GinAR, we propose GinAR+ and enhance its robustness to data with different missing rates. Experiments validate the superiority of GinAR+ and our motivation.

Trust management of network nodes can protect the security of IoT, e.g., detection of false messages and malicious nodes, optimizing IoT services, etc. Trust evaluation and trust prediction are the core contents of trust management, which are used to evaluate the trustworthiness of nodes. However, the lack of trust-related data will lead to the failure of the activation of trust evaluation mechanisms. The update of trust needs to be solved by trust prediction schemes. This paper designs a trust prediction scheme, utilizing neural networks and mathematical calculations to realize the classification and prediction of trust iterations. First of all, this paper gives the detailed architecture of the designed scheme, including the neural network model and data preprocessing. Then analyze the theoretical calculations of related variables in trust iterations, e.g., mean value, variance, etc. Finally, utilizing simulation experiments to verify the performance of the designed trust prediction scheme, including conserved quantity in trust iterations, dataset preprocessing, testing of the scheme, malicious attack resistance analysis, etc. The experiments prove that the scheme can resist related malicious attacks, e.g., data tampering attacks, etc. Moreover, the residual error of trust prediction is not more than 0.0075, which is better than the existing trust prediction schemes.

This paper addresses the problem of scheduling real-time wireless flows with general traffic patterns in dynamic network conditions. The main goal is to maximize the fraction of packets to be delivered within their deadlines, which is referred to as timely-throughput. While scheduling algorithms for frame-based traffic models and greedy maximal scheduling methods like LDF have been thoroughly studied, algorithms providing deadline guarantees on packet delivery for general traffic under dynamic network conditions are insufficient. To address this issue, we present a comparative study of two deep reinforcement learning-based scheduling algorithms: RL-Centralized and RL-Decentralized, which are designed to optimize timely-throughput for real-time wireless flows with general traffic patterns in dynamic wireless networks. The RL-Centralized scheduling algorithm formulates the centralized scheduling problem as a Markov Decision Process (MDP) and leverages a Multi-Environments Dueling Double Deep Q-Network (ME-D3QN) structure to adapt to dynamic network conditions. The RL-Decentralized scheduling problem is formulated as a Multi-Agent Markov Decision Process (MMDP) and employs the Node State Consensus Protocol (NSCP) and Lifelong Reinforcement Learning Decentralized Training and Decentralized Execution (LRL-DTDE) structure to accelerate training. Our experimental results indicate that both proposed algorithms can converge quickly and efficiently adapt to dynamic network conditions with better performance than their baseline policies. Finally, test-bed experiments validate simulation results and confirm that the proposed algorithms are practical on resource-limited platforms.=-1

This paper addresses the problem of scheduling real-time wireless flows under dynamic network conditions and general traffic patterns. The objective is to maximize the fraction of packets of each flow to be delivered within their deadlines, referred to as timely-throughput. The scheduling problem under restrictive frame-based traffic models or greedy maximal scheduling schemes like LDF has been extensively studied so far, but scheduling algorithms to provide deadline guarantees on packet delivery for general traffic under dynamic network conditions are very limited. We propose two scheduling algorithms using deep reinforcement learning approach to optimize timely-throughput for general traffic in dynamic wireless networks: RL-Centralized scheduling algorithm and RL-Decentralized scheduling algo-rithm. Specifically, we formulate the centralized scheduling problem as a Markov Decision Process (MDP) and a multi-environments double deep Q-network (ME-DDQN) structure is proposed to adapt to the dynamic network conditions. The decentralized scheduling problem is formulated as a Partially Observable Markov Decision Process (POMDP) and an expert-apprentice centralized training and decentralized execution (EA-CTDE) structure is designed to accelerate the training speed and achieve the optimal timely-throughput. The extensive results show that the proposed scheduling algorithms converge fast and adapt well to network dynamics with superior performance compared to baseline policies. Finally, experimental tests confirm simulation results and also show that the proposed algorithms are feasible in practice on resource limited platforms.

This paper considers a time-critical task allocation problem in a distributed multi-UAV system. Existing distributed task allocation algorithms trend to increase communication overhead due to the resolution of numerous task-bundle conflicts between UAVs and easily trap into local optimum with greedy strategy. In this work, we propose a novel distributed task allocation method. First, tasks are divided into multiple clusters, and then UAVs build their task bundles from separate task clusters to avoid conflicts between them, thereby reducing communication overhead. Second, to increase the exploratory ability, an improved ant colony optimization algorithm is proposed to achieve task allocation of UAVs from their corresponding task clusters, instead of using greedy-based strategy. Moreover, an inter-cluster adjustment mechanism is proposed to solve unassigned tasks in task clusters to improve task assignment ratio with low communication overhead, which has been verified in our simulations. Extensive simulation results confirm that our method can achieve efficient task allocation solution with high task assignment ratio and low communication overhead when compared with the state-of-the-art algorithms.

In this paper, we address the problem of building adaptive network coding coefficients under dynamic network conditions (e.g., varying link quality and changing number of relays). In existing linear network coding solutions including deterministic network coding and random linear network coding, coding coefficients are set by a heuristic or randomly chosen from a Galois field with equal probability, which can not adapt to dynamic network conditions with good decoding performance. We propose INCdeep, an adaptive Intelligent Network Coding with Deep Reinforcement Learning. Specifically, we formulate a coding coefficients selection problem where network variations can be automatically and continuously expressed as the state transitions of a Markov decision process (MDP). The key advantage is that INCdeep is able to learn and dynamically adjust the coding coefficients for the source node and each relay node according to ongoing network conditions, instead of randomly. The results show that INCdeep has generalization ability that adapts well in dynamic scenarios where link quality is changing fast, and it converges fast in the training process. Compared with the benchmark coding algorithms, INCdeep shows superior performance, including higher decoding probability and lower coding overhead through simulations and experiments.

This paper addresses the problem of UAV positioning and resource allocation under dynamic network conditions and under instantaneous communication demands in FANETs. We propose MPRdeep, an adaptive, deep reinforcement learning (DRL) approach considering several QoS requirements concurrently. MPRdeep learns to optimize relay UAVs’ positions and forwarding probabilities to minimize reliability-achieving delay and reliability-achieving energy consumption. The key advantage is that MPRdeep is able to learn and dynamically adjust the node positioning and resource allocation according to ongoing network conditions. The results show that MPRdeep converges fast and has generalization ability that adapts well under dynamic network conditions and dynamic locations of users. Compared with baseline methods, MPRdeep shows superior performance in terms of lower reliability-achieving delay and lower reliability-achieving energy consumption via simulations and experiments.

The mobile robotic network consisting multiple robotic devices such as unmanned aerial vehicles (UAVs) is a high-speed mobile wireless network. Existing mobile ad hoc protocols cannot meet the demands of mobile robotic networks due to intermittently connected links and frequent topology changes. This paper proposes a deep reinforcement learning based adaptive and reliable routing protocol, ARdeep. We formulate routing decisions with a Markov Decision Process model to automatically characterize the network variations. To better infer network environment, the link status is considered when making routing decisions. Simulation results demonstrate that ARdeep outperforms the existing good performing QGeo and conventional GPSR.

In many industrial wireless networks, sensor nodes sense an event and then generate related data packets to transmit. In such event-driven networks, contention medium access control (MAC) protocols such as 802.11 DCF are widely used to address the burst traffic. However, due to the fixed probability distribution of contention time-slot selection (PDCS), traditional methods may cause severe collisions when a great number of packets are being transmitted in a short period. To deal with this problem, a MAC protocol that dynamically adjusts PDCS and contention window simultaneously is proposed in this paper. This method exploits the spatial and temporal relationship of packet generation among source nodes in industrial wireless networks. In this method, sensor nodes related to the same event source converge to the optimal PDCS quickly and collisions are significantly reduced. Delay performances of this method are evaluated through simulations. According to the simulation, our protocol outperforms current MAC protocols in access delay by about 20% in event-driven networks.

A network with reliable and rapid communication is critical for Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) consisting of UAVs is a new paradigm of wireless communication . However, the highly dynamic topology of FANETs and limited energy of UAVs have brought great challenges to the routing design of FANETs. It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs. Moreover, few of existing routing protocols simultaneously meet the requirement of low delay and low energy consumption of FANETs. This paper proposes a novel Q-learning based Multi-objective optimization Routing protocol for FANETs to provide low-delay and low-energy service guarantees. Most of existing Q-learning based protocols use a fixed value for the Q-learning parameters. In contrast, Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANETs. In addition, a new exploration and exploitation mechanism is also proposed to explore some undiscovered potential optimal routing path while exploiting the acquired knowledge. Instead of using past neighbor relationships, the proposed method re-estimates neighbor relationships in the routing decision process to select the more reliable next hop. Simulation results show that the proposed method can provide higher packet arrival ratio, lower delay and energy consumption than existing good performing Q-learning based routing method.

Inference efficiency is the predominant design consideration for modern machine learning accelerators. The ability of executing multiply-and-accumulate (MAC) significantly impacts the throughput and energy consumption during inference. However, MAC operation suffers from significant ineffectual computations that severely undermines the inference efficiency and must be appropriately handled by the accelerator. The ineffectual computations are manifested in two ways: first, zero values as the input operands of the multiplier, waste time and energy but contribute nothing to the model inference; second, zero bits in nonzero values occupy a large portion of multiplication time but are useless to the final result. In this article, we propose an ineffectual-free yet cost-effective computing architecture, called split-and-accumulate (SAC) with two essential bit detection mechanisms to address these intractable problems in tandem. It replaces the conventional MAC operation in the accelerator by only manipulating the essential bits in the parameters (weights) to accomplish the partial sum computation. Besides, it also eliminates multiplications without any accuracy loss, and supports a wide range of precision configurations. Based on SAC, we propose an accelerator family called Tetris and demonstrate its application in accelerating state-of-the-art deep learning models. Tetris includes two implementations designed for either high performance (i.e., cloud applications) or low power consumption (i.e., edge devices), respectively, contingent to its built-in essential bit detection mechanism. We evaluate our design with Vivado HLS platform and achieve up to 6.96× performance enhancement, and up to 55.1× energy efficiency improvement over conventional accelerator designs.

Communications in industrial applications like wireless factory automation demands different timing requirements. Providing timely medium access of the critical traffic and its prioritization over regular traffic is a significant challenge in industrial wireless networks. Successive Interference Cancellation (SIC) technique is an effective way to decrease access delay by allowing multiple transmissions concurrently. A series of novel Medium Access Control (MAC) protocols are proposed to differentiate access delay for various traffic types or only exploit SIC for unique traffic type. However, to the best of our knowledge, this work is the first priority-based distributed MAC protocol that employs SIC (PDMAC-SIC) to provide low delay and accommodate different types of traffic for industrial wireless networks. There are two major contributions of our work: first, an extra power contention procedure other than traditional RTS/CTS contention in CSMA/CA is introduced in our PDMAC-SIC. This power contention procedure allows multiple transmitters to access the same channel simultaneously and thus access delay is decreased. Second, PDMAC-SIC is modeled by Markov chain and then the access delay is minimized by optimizing the size of power contention window. Our analytical model is verified through simulation. Results reveal that PDMAC-SIC performs better on access delay and packet loss rate than the existing good performing priority based CSMA/CA.

Wireless networks have become a very attractive solution for soft real-time data transport in the industry. For such technologies to carry real-time traffic, reliable bounds on end-to-end communication delays have to be ascertained to warrant a proper system behavior. As for legacy wired embedded and real-time networks, two main wireless multiple access methods can be leveraged: one is time division multiple access (TDMA), which follows a time-triggered paradigm, and the other is carrier sense multiple access with collision avoidance (CSMA/CA), which follows an event-triggered paradigm. This paper proposes an analytical comparison of the time behavior of two representative TDMA and CSMA/CA protocols in terms of the worst-case end-to-end delay. This worst-case delay is expressed in a probabilistic manner because our analytical framework captures the versatility of the wireless medium. Analytical delay bounds are obtained from delay distributions, which are compared to fine-grained simulation results. Exhibited study cases show that TDMA can offer smaller or larger worst-case bounds than CSMA/CA depending on its settings.

With the rapid development of marine technology, underwater sensor networks (UWSNs) are gradually evolving from research to practice in recent years. Practicability and reliability are two major concerns for routing protocols in UWSNs. As localization is not necessary in depth-based routing protocol (DBR), it has an outstanding practicability than other geographic routing protocols. However, the reliability is not well ensured. In this paper, we propose an innovative depth-based routing with network coding improving routing reliability while preserving the intrinsic distributed manner of DBR and introducing little time delay and energy cost. Moreover, a simple analytical performance model where ideal MAC is assumed is proposed to derive the analytical delivery ratio for our DBR-NC and DBR protocols. This analytical model is validated by simulation results. The extensive simulation results show that the proposed DBR-NC protocol outperforms (over 15%) the state of art DBR protocols in terms of packet delivery ratio. We also show that our DBR-NC will not introduce much extra delay and energy consumptions.

Wireless networks have become a very attractive solution for soft real-time data transport in the industry. For such technologies to carry real-time traffic, reliable bounds on end-to-end communication delays have to be ascertained to warrant a proper system behavior. As for legacy wired embedded and real-time networks, two main wireless multiple access methods can be leveraged: (i) time division multiple access (TDMA), which follows a time-triggered paradigm and (ii) Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which follows an event-triggered paradigm. This paper proposes an analytical comparison of the time behavior of two representative TDMA and CSMA/CA protocols in terms of worst-case end-to-end delay. This worst-case delay is expressed in a probabilistic manner because our analytical framework captures the versatility of the wireless medium. Analytical delay bounds are obtained from delay distributions, which are compared to fine-grained simulation results. Exhibited study cases show that TDMA can offer smaller or larger worst-case bounds than CSMA/CA depending on its settings.