Abstracts:Hadoop provides a powerful platform that allows reliable, scalable, and distributed processing of massive data sets across a cluster of computers. Log data record events taken place in the Hadoop system that helps to understand system activities and diagnose problems. However, system upgrades and updates often change the syntax and patterns of logs, rendering the machine-learning models that were designed for the legacy system ineffective. Retraining the machine-learning models with new data sets from scratch might improve the accuracy of the machine-learning model. Nevertheless, annotating new data sets is often time consuming and labor intensive. In this article, we propose a domain adaptive log anomaly prediction framework called LogAT to effectively transfer learned knowledge from the existing labeled data set (source domain) to the new unlabeled data set (target domain) by adopting an unsupervised domain adaption method. Furthermore, a hierarchical anomaly knowledge graph has been constructed to represent the domain knowledge that facilitates the subsequent detection and diagnosis of system faults. Extensive experiments have been conducted on public and real-world data sets to validate the effectiveness of the proposed framework as well as each module. Our results show that LogAT achieves superior performance over the state-of-the-art methods for predicting log anomalies and acquiring considerable performance improvement in terms of AUC-ROC score on different Hadoop application data sets.

Abstracts:Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

Abstracts:In this article, a deep reinforcement learning (DRL) control scheme is proposed to satisfy the strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication (URLLC) and enhanced mobile broadband (eMBB) using 5G multiple radio access technology (RAT)-based partial offloading and multiaccess edge-computing (MEC) resource allocation. In the proposed scheme, the user equipment (UE) makes optimal offloading decisions while the MEC server dynamically adjusts the server resources based on offloading requests from multiple UEs using DRL technology. The aim of the proposed scheme is to minimize the energy consumption of the UEs while maximizing the system utility (SU) performance, which is composed of the spectral efficiency (SE) and offloading success rate (OSR) of the MEC server. In addition, multiagent distributed learning technology and best experience push (BEP) techniques are used to enhance the learning efficiency of the DRL framework. The simulation result shows that the proposed scheme provides an improved SU and energy consumption performance compared to the benchmark offloading schemes.

Abstracts:The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and universities make every endeavor to enhance the user experience (UX) of WiFi networks where the network dynamics (e.g., traffic load and user mobility) are usually unexpected. To this end, an existing mechanism for WiFi association is client driven, i.e., users associate themselves to the AP with higher signal strength. However, they still incur dissatisfaction due to the insufficient available bandwidth. To cope with this in a centralized manner, we propose DARCAS, a software-defined network (SDN)-enabled WiFi framework for association regulation. DARCAS adopts a notion of bandwidth satisfaction ratio (BSR), which is closely related to UX. It maximizes the aggregated network throughput while satisfying the BSR of each user with sufficient airtime (i.e., channel occupancy time) provision. We use this idea in a metaheuristic genetic algorithm called DARCAS-GA, which effectively finds the suboptimal association distribution of the maximum BSR in polynomial time. We implement the DARCAS system on off-the-shelf wireless routers and an SDN controller. We report real-life experimental results in the considered scenarios and conduct extensive simulations on the NS-3 simulator to examine its performance with scalability. With fine-tuned settings, DARCAS exhibits up to 80% of the BSR gain compared to existing solutions.

Abstracts:Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.

Abstracts:The location awareness becomes increasingly important as mobile devices such as smartphones are used extensively in our daily lives. Existing indoor localization solutions either require certain preinstalled infrastructures or add-on devices, which could not provide a location semantics identification service for smartphones to infer both type and size of a geographic location. In this work, we propose a new active sensing system that enables smartphones to identify its location semantics without requiring any additional infrastructure. The main idea behind our system is to utilize the acoustic signatures, which are derived from the smartphone by emitting a predesigned beep signal and identifying two echo sets which correspond to sidewalls and other static objects respectively, as the proof to achieve both spatial size estimation and room-type prediction simultaneously for indoor location semantics identification. Given the microphone samplings, our system designs a correlation-based scheme to identify beginning points of echoes corresponding to static reflectors accurately from the received signal. To achieve an accurate location semantics identification, we develop a new echo selection scheme to discriminate echoes created by sidewalls and other static reflectors by utilizing the geometrical relationships between the delays of echoes. To deal with the varying number of identified echoes, our location semantics prediction scheme then derives histograms from echo sets and adopt a deep-learning-based classifier to determine the current location semantics. Our experimental results show that our proposed system is accurate and robust for location semantics identification under various real-world scenarios.

Abstracts:The millimeter-wave (mmWave) technology can support the wide-band communication requirements for the environment raw perception information sharing to guarantee the safety of connected automated vehicles (CAVs). But, how to achieve the fast and robust mmWave beam tracking is a challenging problem in a high mobility CAVs scenario. Therefore, this article proposes a camera-sensing-assisted joint offline and online beam tracking (CS-JBT) algorithm in the mmWave frequency band. First, the mobility model is designed to predict the next moment of CAV and reduce the beam searching space overhead. In the offline learning phase of the proposed CS-JBT algorithm, the optimal beam pair under the current mobility state is obtained to guarantee the timeliness of the beam tracking process. Furthermore, to solve the mobility state deviation problem caused by the camera sensing error, the online learning phase is designed to achieve the optimal beam tracking performance efficiently for the practical scenario. Simulation and hardware testbed results verify that the proposed CS-JBT algorithm can minimize the beam tracking latency from 500 to 20 ms in contrast to the existing VBC-PF algorithm, while achieving a stable throughput over 2.5 Gb/s in the 28-GHz mmWave frequency band.

Abstracts:Accurate calibration of low-cost environmental sensors is a prerequisite for their successful use in many monitoring applications. State-of-the-art calibration methods vary from simple linear regression to sophisticated deep models based on LSTMs and GRUs. The latter take past measurements to improve calibration accuracy. In this article, we argue that both recent past and close future measurements help to achieve accurate calibration, whereas accuracy improvements beyond the past come with a delay introduced by the occurrence of the future. We propose a generalized many-to-many calibration scheme called SensorFormer based on the successful Transformer model which takes both past and future raw measurements into account. We show that the proposed approach: 1) outperforms other methods by improving calibration accuracy by 16.5%–20.4% on public data sets and own field data and 2) can efficiently run on low-power microcontrollers with very limited computational and storage capabilities. The latter is achieved by a novel optimization technique based on learnable input subsampling taking advantage of the properties of typical sensor data. We manage to reduce the model size by 20%–33% and minimize the overall floating point operations per second (FLOPs) by 65% while maintaining superior accuracy than state-of-the-art methods.

Abstracts:Solar insecticidal lamps Internet of Things (SIL-IoT) is a new green prevention and control technology for pest management. In the implementation of SIL-IoT to large-scale regions, two practical issues remain to be solved, that is: 1) scheduling the cleaning tasks of SILs periodically and 2) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, mobile crowdsensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This article proposes an MCS-enabled framework to address the SIL maintenance problem (SILMP) and perform system analysis by considering both the partition structure of farmland and the insecticidal efficiency of SILs. In addition, considering the farmland’s practical natural geographical features, we propose dividing the regions of interest into numerous subareas, where each subarea can be considered a separate partition. Finally, we formulate the SILMP framework as two subproblems, i.e., path planning and task selection, and propose two different methods to tackle each problem based on the concept of greedy algorithm. Simulation results show that our proposed methods have improved performance in the tradeoff between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.