Poor Communication Environment Research Articles

Lyapunov-guided deep reinforcement learning for delay-aware online task offloading in MEC systems

With the arrival of 5G technology and the popularization of the Internet of Things (IoT), mobile edge computing (MEC) has great potential in handling delay-sensitive and compute-intensive (DSCI) applications. Meanwhile, the need for reduced latency and improved energy efficiency in terminal devices is becoming urgent increasingly. However, the users are affected by channel conditions and bursty computational demands in dynamic MEC environments, which can lead to longer task correspondence times. Therefore, finding an efficient task offloading method in stochastic systems is crucial for optimizing system energy consumption. Additionally, the delay due to frequent user–MEC interactions cannot be overlooked. In this article, we initially frame the task offloading issue as a dynamic optimization issue. The goal is to minimize the system’s long-term energy consumption while ensuring the task queue’s stability over the long term. Using the Lyapunov optimization technique, the task processing deadline problem is converted into a stability control problem for the virtual queue. Then, a novel Lyapunov-guided deep reinforcement learning (DRL) for delay-aware offloading algorithm (LyD2OA) is designed. LyD2OA can figure out the task offloading scheme online, and adaptively offload the task with better network quality. Meanwhile, it ensures that deadlines are not violated when offloading tasks in poor communication environments. In addition, we perform a rigorous mathematical analysis of the performance of Ly2DOA and prove the existence of upper bounds on the virtual queue. It is theoretically proven that LyD2OA enables the system to realize the trade-off between energy consumption and delay. Finally, extensive simulation experiments verify that LyD2OA has good performance in minimizing energy consumption and keeping latency low.

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

Association between the telecommuting environment and somatic symptoms among teleworkers in Japan.

This study aimed to clarify the association between telecommuting environments and somatic symptoms among teleworkers in Japan. This cross-sectional study, conducted from September 27 to October 29, 2021, used data from the Japan COVID-19 and Society Internet Survey (JACSIS study) in Japan. Of the 31 000 male and female respondents, who were Japanese residents aged 15-79years and were randomly selected from the panel members of an internet survey company, 4569 home-based teleworkers were finally included in the analysis; 26 431 respondents who met the exclusion criteria were excluded. The analysis included 4 cut-offs (≥4, 8, 12, and 16 points) for somatic symptoms on the Somatic Symptom Scale-8 as objective variables, and the telecommuting environment, such as having adequate desk light and a quiet environment, as explanatory variables. Adjusted Poisson regression analysis was conducted using demographic variables as covariates. The prevalence ratio (PR) for somatic symptoms increased significantly as the number of poor telecommuting conditions increased, regardless of the cut-off value for somatic symptoms or the frequency of teleworking. In the telecommuting environment, the PR for somatic symptoms was significantly higher for the following 6 items: poor teleworking space to concentrate, inadequate foot space, poor communication environment, poor space for relaxation, noise, and inappropriate temperature and humidity. These results suggest that for home-based teleworkers, the more inadequate the telecommuting environment, especially in the aforementioned 6 areas, the higher the likelihood of somatic symptoms. Improving these environments may be useful in preventing various somatic symptoms.

Task Assignment of UAV Swarms Based on Auction Algorithm in Poor Communication Environments

Poor communication environments always lead to unstable communication in unmanned aerial vehicle swarms. To solve the problem of task assignment in poor communication environments, this study proposed an information fusion strategy (IFS) based on information integrity and authenticity. The proposed IFS was embedded into the classical sequential and the Prim assignment and its generalization (G-Prim) decentralized task assignment algorithms, and these two improved variants with the proposed IFS were denoted as sequential auction with IFS (Seq-IFS) and G-Prim-IFS, respectively. The Bernoulli and Gilbert–Elliott models, which can model communication delay and packet loss, were adopted to describe unstable communication channels. A series of test instances with different swarm sizes and levels of communication channel reliability was used to test the performances of Seq-IFS and G-Prim-IFS in their original forms. Numerical experimental results demonstrated that the proposed Seq-IFS and G-Prim-IFS significantly outperformed their original versions in most test instances, particularly in cases with low communication environments.

An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals

With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. The mechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.

Optimal Control for Unmanned Systems With One-Way Broadcast Communication

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.

Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

A Safety Reinforced Cooperative Adaptive Cruise Control Strategy Accounting for Dynamic Vehicle-to-Vehicle Communication Failure.

Cooperative Adaptive Cruise Control (CACC) is an advanced technique for organizing and managing a vehicle platoon, which employs the Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I, or V2X) wireless communication to minimize the inter-vehicle distance while guaranteeing string-stability. Consequently, the conventional CACC system relies heavily on the quality of communications, which means that the regular CACC platoon is sensitive to the communication failure. Therefore, in this paper, a Safety Reinforced Cooperative Adaptive Cruise Control (SR-CACC) strategy is proposed to resist unexpected communication failure. Different from the regular CACC system, the safety enhanced platoon control system is embedded with a dual-branch control strategy. When a fatal wireless communication failure is detected and confirmed, the SR-CACC system will automatically activate the alternative sensor-based adaptive cruise control strategy. Moreover, to make the transforming process smooth, a linear smooth transition algorithm is added to the SR-CACC system. Then, to verify the performance of the proposed SR-CACC system, we conducted a simulation experiment with a heterogonous platoon constructed with eight vehicles. The experiments results reveal that, under the extremely poor communication environment, the proposed SR-CACC strategy can significantly improve the safety performance of the organized vehicle platoon.

Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing.

To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives.

Optimization of base station density and user transmission power in multi-tier heterogeneous cellular systems

In this paper, a loss minimization issue is proposed, which includes both cost of user power consumption and base station (BS) deployment. A multi-tier heterogeneous network (HetNet) is considered and modeled with random geometry of Poisson point process (PPP). BSs and users are distributed randomly following PPP with the specific densities. Cutoff threshold (ρk) as the average received power at the kth tier BS is proposed to model uplink channels. The kth tier user transmission power is formulated through a channel inversion power control. Tier Selection Probability indicating the possibility of a user accessing a certain tier is derived. Bias (Bk) is proposed to change the kth tier Tier Selection Probability. Thus, an operator is able to change bias based on nearby environment and communication status. Also, Truncated Outage Probability and signal-to-interference ratio (SINR) Outage Probability are derived, which represents a connection failure status. Truncated outage results from insufficient power supply at users and SINR outage results from poor communication environment. Genetic algorithm (GA) is introduced to determine the trade off between outage constraints and the loss minimization issue. Simulation shows that, when equipped with equal bias, multi-tier network has smaller Truncated Outage Probability but larger SINR Outage Probability than single-tier network. On the contrary, with different bias, Truncated Outage Probability in multi-tier is larger than the single-tier case and SINR Outage Probability in multi-tier is less than that in single-tier. A considerable loss reduction can be witnessed in both cases.

IoT-Based Strawberry Disease Prediction System for Smart Farming.

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.

Delay Tolerant Navigation Method for Fast Evacuation in Poor Communication Environment at the Time of Disaster

It is very important to evacuate quickly to evacuation sites in order to minimize the number of victims in the event of a disaster. However, since only information about the locations of evacuation sites is presented before a disaster, in the event of a disaster, people present at various places will not be sure about the evacuation routes that they should take individually. Accordingly, there is a need for a method for quickly determining evacuation routes and guiding people in accordance with the status of the locations of evacuation sites, the location of impassable road section, and the congestion caused by vast number of evacuees. However, in poor communication environments such as disaster area, since information distribution is very difficult and delays are inevitable, a method for determining evacuation routes must consider delays in information distribution. In this paper, we propose a method of determining evacuation guidance routes in consideration of delays that occur in information distribution, and we evaluates the influence of the delays on evacuation guidance and the performance of the proposed method through simulation.

An Analytical Approach to Relay Access Delay in Multihop Relay Chain of VANETs

On the demand of many applications, vehicle to vehicle (V2V) communications have attracted many researchers’ attentions. In the developments of traffic safety applications based on VANET, the dissemination delay is critical. The dissemination delay of safety messages in the form of text has been studied thoroughly. In contrast, the dissemination delays of video or audio safety messages are just emerging and there are still a lot of things to be done. Considering the poor communication environment of multimedia safety messages dissemination in V2V, the analysis of multimedia message dissemination delay has great significance in the traffic safety applications. By extending and integrating previous achievements of analyzing MAC access delay in video stream dissemination, we proposed a method for modeling the relay access delay of IEEE 802.11p in the multihop relay chain of VANETs. Simulation results showed that the cumulative distribution function, the expectation, and the standard deviation of the relay access delays can be calculated effectively. In addition, the impacts of the number of hops and the length of packets on the relay access delays have been properly evaluated. Our study would open these research issues for discussion, which will contribute to the design and implementation of traffic safety.

Commentary on “The People in Control Conference at City University, London: ‘We have opinions to share about our lives’”

PurposeThe purpose of this article is to provide a commentary on Harding et al.'s article about the involvement of people with learning disabilities in university teaching and conference presentations.Design/methodology/approachThe article reviews research into communication strengths and needs and into communication partnerships between service users and members of staff with a view to identifying communication barriers to the involvement of people with learning disabilities.FindingsThe generally poor communication environments in which people are supported are likely to limit opportunities for involvement.Originality/valueIt is suggested that increased involvement is likely to result from increased quality of daily communication. This may best occur as part of general service‐wide approaches to improving service quality, such as person‐centred active support.

Optimization Algorithm for Energy-efficient Wireless Sensor Network Design Problem

Wireless sensor networks hold promise as a way of monitoring the structural health of railway structures, especially those that are underground or otherwise difficult to access. In this paper, effective algorithms for designing wireless sensor networks are investigated. The authors review optimization problems arising in railway system and highlight the importance of minimizing the total cost of the wireless sensor network in a general communication environment while maximizing the network lifetime in a poor communication environment. A life span algorithm for the network lifetime maximization problem and a Lagrangian heuristic algorithm for the total cost minimization problem are proposed. The effectiveness of these algorithms was verified using randomly generated data and data acquired from a real wireless sensor network. The results indicate that the proposed algorithms can be effectively used in the design of wireless sensor networks for railway structural health monitoring.

Improving the coordination efficiency of limited‐communication multi–autonomus underwater vehicle operations using a multiagent architecture

AbstractThis research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An intelligent mission executive has been created that uses multiagent technology to control and coordinate multiple AUVs in communication‐deficient environments. By incorporating real‐time vehicle prediction, blackboard‐based hierarchical mission plans, mission optimization, and a distributed multiagent–based paradigm in conjunction with a simple broadcast communication system, this research aims to handle the limitations inherent in underwater operations, namely poor communication, and intelligently control multiple vehicles. In this research, efficiency is evaluated and then compared to the current state of the art in multiple AUV control. The research is then validated in real AUV coordination trials. Results will show that compared to the state of the art, the control system developed and implemented in this research coordinates multiple vehicles more efficiently and is able to function in a range of poor communication environments. These findings are supported by in‐water validation trials with heterogeneous AUVs. © 2010 Wiley Periodicals, Inc.

A 22 month study of patient complaints at a National Health Service hospital

Patient complaints are an important source of information for service improvements. We audited patient complaints made about medical care in a National Health Service District general hospital over a 22 month period. Complaints were about medical care, nursing care, attitudes of staff, poor communication, clinical delay (9%) and hospital environment. The complaints department closed 66% complaints within 20 days. The majority of the complaints were directly related to clinical care, poor communication, attitudes of staff and nursing care. However, 99% of patients were satisfied with an explanation and an apology indicating that almost all have been due to a lack of good communication than due to real deficiencies in the clinical care. The hospital management has investigated the majority of cases within 20 days and has made several policy changes after the investigations.