Abstract Marine Internet of Things (IOT) is the use of Internet technology to connect various sensing devices at sea, so as to integrate maritime information and realize the monitoring and systematic management of complex data at sea. The marine environment is complex and changeable, and marine disasters occur frequently, such as red tides. Due to the sudden and destructive nature of red tide, it plays a pivotal role to monitor the occurrence of the red tide for the marine IoT, where machine learning has been widely used to predict red tides. However, they were rarely able to catch the sudden change of chlorophyll‐a, which has important practical significance for predicting the occurrence of red tide. In order to deal with the above problems, this paper proposes the triple‐stage attention‐based recurrent neural network, which can enhance the representation ability of input sequences, selectively capture dynamic spatial correlations between input multi‐channel observations in the input sequence, meanwhile adaptively capturing dynamic temporal correlations between different time intervals in the input sequence. The results show that this method outperforms the state‐of‐art baseline methods here.

Abstract The reconfigurable capability of static random‐access memory (SRAM) field programmable gate array (FPGA) can be used for its fault self‐repair method. As a machine learning method, the genetic algorithm (GA) is an FPGA fault repair method that can be automatically executed on‐orbit without any ground support. However, the GA‐based fault repair method has disadvantages, such as the dependency on processors, the knowledge requirement for user designs in FPGAs, and the small size of repaired circuits. To address these issues, this paper presents a comprehensive analysis of the FPGA bitstream in the aerospace industry. An accurate on‐orbit fault location can be identified by bitstream copying and exhaustive test and the executed area of the GA can be reduced to one tile. In addition, the probability function of the algorithm is optimized, which converts floating‐point operations into integer arithmetic operations that are easily implemented in FPGAs without processors. The method is outstanding compared with existing ones, considering: (1) The size of repaired circuits is hundreds of times larger than those from other methods. (2) Its implementations are totally up to FPGAs' own logic, with no requirement for processors. (3) There is no knowledge requirement for user design. (4) It reaches the leading level with a success rate of 81%–93%. The method has been verified by various applications in XC7VX330T, which demonstrates its engineering practicability.

Due to ubiquitous increase of mobile services and powerful Internet-of-Things (IoT) devices, the interest for mobile-edge computing (MEC) solutions has grown both in industry and academia. One of the fundamental mechanisms of MEC is offloading, i.e., delegation of a computation from the user to a server (set) placed near to the edge. Edge servers may have poor incentives to run a delegated service, for example, for the temporary limited resources. In this article, we dissect the incentive mechanisms within a MEC ecosystem with the aim of ensuring a fault-tolerant edge service under unreliable scenarios. In particular, we design an auction mechanism to model the interaction between the MEC players, and model the edge users’ probability of successful offloading, assuming that the cost of executing each offloading request is private. Scrutinizing the demand uncertainty of edge users, the main motive of our auction method is to optimize the offloading cost to engage more edge users in this process, while imposing probabilistic guarantees of offloading service execution. Our offloading cost minimization problem is considered to be an NP-hard. For the solution, we use a heuristic methodology to get the optimal approximation ratio and provide economical fairness. We provide exhaustive simulation results to show the excellent performance of our scheme.

In this paper, we propose a novel flow rule matching framework, DeepMatch, in Software-Defined Networking (SDN) to provide a fine-grained traffic flow measurement capability. Specifically, the flow rule matching control at a particular SDN switch is examined to maximize the traffic flow granularity degree while proactively protecting the flow-table in the switch from being overflowed. This control process is supervised by a control module referred to as DeepMatch instance. Regarding this instance, an optimization problem is formulated based on a Markov decision process (MDP) and a Partially Observable Markov decision process (POMDP), respectively. We develop a deep dueling neural network based flow rule matching control algorithm to solve the optimization problem, thereby quickly attaining a significant traffic flow granularity level and eliminating the switch flow-table overflow problem. Furthermore, we propose an experience data sharing (EDS) mechanism that enables a new instance to learn faster about the flow rule matching control. The results of our performance evaluation show that, by applying the DeepMatch framework in a highly dynamic traffic scenario, the traffic flow granularity degree at the access and the core switches increases by 24.0% and 31.63%, respectively, compared to the FlowStat method. DeepMatch is also highly outperforming the ReWiFlow, SDN-Mon, and Exact-Match approaches. In addition, by employing the EDS mechanism, a new instance can reduce its learning time up to 46.42% for supervising an access switch and up to 37.50% for supervising a core switch.

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep${Q}$-Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

The following topics are dealt with: learning (artificial intelligence); telecommunication traffic; virtualisation; resource allocation; Internet of Things; software defined networking; Internet; computer network management; mobile computing; 5G mobile communication.

Network congestion is a key challenge in resource‐constrained networks, particularly those with limited bandwidth to accommodate high‐volume data transmission, which causes unfavorable quality of service, including effects such as packet loss and low throughput. This challenge is crucial in wireless sensor networks (WSNs) with restrictions and constraints, including limited computing power, memory, and transmission due to self‐contained batteries, which limit sensor node lifetime. Determining a path to avoid congested routes can prolong the network. Thus, we present a path determination architecture for WSNs that takes congestion into account. The architecture is divided into 3 stages, excluding the final criteria for path determination: (1) initial path construction in a top‐down hierarchical structure, (2) path derivation with energy‐aware assisted routing, and (3) congestion prediction using exponential smoothing. With several factors, such as hop count, remaining energy, buffer occupancy, and forwarding rate, we apply fuzzy logic systems to determine proper weights among those factors in addition to optimizing the weight over the membership functions using a bat algorithm. The simulation results indicate the superior performance of the proposed method in terms of high throughput, low packet loss, balancing the overall energy consumption, and prolonging the network lifetime compared to state‐of‐the‐art protocols.

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.