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Abstract: The current specification of the IEEE 802.15.4 standard for beacon-enabled wireless sensor networks does not define how the fraction of the time that wireless nodes are active, known as the duty cycle, needs to be configured in order to achieve the optimal network performance in all traffic conditions. The work presented here proposes a duty cycle learning algorithm (DCLA) that adapts the duty cycle during run time without the need of human intervention in order to minimise power consumption while balancing probability of successful data delivery and delay constraints of the application. Running on coordinator devices, DCLA collects network statistics during each active duration to estimate the incoming traffic. Then, at each beacon interval uses the reinforcement learning (RL) framework as the method for learning the best duty cycle. Our approach eliminates the necessity for manually (re-)configuring the nodes duty cycle for the specific requirements of each network deployment. This presents the advantage of greatly reducing the time and cost of the wireless sensor network deployment, operation and management phases. DCLA has low memory and processing requirements making it suitable for typical wireless sensor platforms. Simulations show that DCLA achieves the best overall performance for either constant and event-based traffic when compared with existing IEEE 802.15.4 duty cycle adaptation schemes. [Copyright &y& Elsevier]

Abstract: Wireless sensor networks are a key enabling technology for industrial monitoring applications where the use of wireless infrastructure allows high adaptivity and low cost in terms of installation and retrofitting. To facilitate the move from the current wired designs to wireless designs, concerns regarding reliability must be satisfied. Current standardization efforts for industrial wireless systems lack specification on efficient routing protocols that mitigate reliability concerns. Consequently, this work presents the InRout route selection algorithm, where local information is shared among neighbouring nodes to enable efficient, distributed route selection while satisfying industrial application requirements and considering sensor node resource limitations. Route selection is described as a multi-armed bandit task and uses Q-learning techniques to obtain the best available solution with low overhead. A performance comparison with existing approaches demonstrates the benefits of the InRout algorithm, which satisfies typical quality of service requirements for industrial monitoring applications while considering sensor node resources. Simulation results show that InRout can provide gains ranging from 4% to 60% in the number of successfully delivered packets when compared to current approaches with much lower control overhead. [Copyright &y& Elsevier]

Wireless Sensor Networks (WSNs) have a broad application range in the area of monitoring and surveillance tasks. Among these tasks, disaster detection or prevention in environmental scenarios is one typical application for WSN. Disasters may for example be forest fires, volcano outbreaks or flood disasters. Here, the monitored events have the potential to destroy the sensor devices themselves. This has implications for the network lifetime, performance and robustness. While a fairly large body of work addressing routing in WSNs exists, little attention has been paid to the aspect of node failures caused by the sensed phenomena themselves. This paper presents a proactive routing method that is aware of the node’s destruction threat and adapts the routes accordingly, before node failure results in broken routes, delay and power consuming route re-discovery. The performance of the presented routing scheme is evaluated and compared to OLSR based routing in the same scenario. [ABSTRACT FROM AUTHOR]

This article presents a survey of service-oriented architecture and Web services within telecommunications. Telecommunications over the past few years has been in a state of constant flux, with shifts in regulations, increased competition, and technological progress. The article describes these forces in detail and shows that the need for successful adoption of SOA within telecommunication has become a major agenda item for the majority of telecommunication network operators. The article then proceeds to describe SOA and its key enablers in telecommunications - Web services, event-driven architectures, Parlay X/ECMA specifications, and the enterprise service bus. [ABSTRACT FROM AUTHOR]

The article focuses on research and development activities at the Cork Institute of Technology in Ireland. Accordingly, the research activities of the institute is an important aspect of its interaction with the engineering industry. Research activities are organized through its main channels such as industry and technology centers as well as specialist research groups. Some of the standard courses offered at the technology centers are mentioned.

LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. These devices are often deployed in remote areas where the end-to-end connectivity provided through one or more gateways may be limited. In this paper, we examine the case where the gateway is not available at all times. As a consequence, the sensing data need to be buffered locally and transmitted as soon as a gateway becomes available. However, due to the Aloha-style transmission policy of current LoRa-based standards, such as the LoRaWAN, delivering a large number of packets in a short period of time by a large number of nodes becomes impossible. To avoid bursts of collisions and expedite data collection, we propose a time-slotted transmission scheduling mechanism. We formulate the data scheduling optimisation problem, taking into account LoRa characteristics, and compare its performance to low complexity heuristics. Moreover, we conduct a set of simulations to show the benefits of synchronous communications on the data collection time and the network performance. The results show that the data collection can reliably be achieved at least 10 times faster compared to an Aloha-based approach for networks with 100 or more nodes. We also develop a proof-of-concept to assess the overhead cost of communicating the schedule to the nodes and we present experimental results. [ABSTRACT FROM AUTHOR]

The use of renewable energy has increased dramatically over the past couple of decades. Wind farms, consisting of wind turbines, play a vital role in the generation of renewable energy. For monitoring and maintenance purposes, a wind turbine has a variety of sensors to measure the state of the turbine. Sensor measurements are transmitted to a control center, which is located away from the wind farm, for monitoring and maintenance purposes. It is therefore desirable to ensure reliable wireless communication between the wind turbines and the control center while integrating the observations from different sensors. In this paper, we propose an IoT based communication framework for the purpose of reliable communication between wind turbines and control center. The communication framework is based on repeat-accumulate coded communication to enhance reliability. A fusion algorithm is proposed to exploit the observations from multiple sensors while taking into consideration the unpredictable nature of the wireless channel. The numerical results show that the proposed scheme can closely predict the state of a wind turbine. We also show that the proposed scheme significantly outperforms traditional estimation schemes. [ABSTRACT FROM AUTHOR]

In this paper we present enhanced routing protocol for low-lower and lossy networks (ERPL), a reduced overhead routing protocol for short-range low-power and lossy wireless networks, based on RPL. ERPL enhances peer-to-peer (P2P) route construction and data packet forwarding in RPL's storing and non-storing modes of operation (MoPs). In order to minimize source routing overhead, it encodes routing paths in Bloom Filters (BF). The salient features of ERPL include the following: (i) optimized P2P routing and data forwarding; (ii) no additional control messages; and (iii) minimized source routing overhead. We extensively evaluated ERPL against RPL using emulation, simulation, and physical test-bed based experiments. Our results demonstrate that ERPL outperforms standard RPL in P2P communication and its optimized P2P route construction and data forwarding algorithms also positively impact the protocol's performance in multi-point to point (MP2P) and point to multi-point (P2MP) communications. Our results demonstrate that the BF-based approach towards compressed source routing information is feasible for the kinds of networks considered in this paper. The BF-based approach results in 65% lower source routing control overhead compared to RPL. Our results also provide new insights into the performance of MP2P, P2MP, and P2P communications relative to RPL's destination-oriented directed a-cyclic graph (DODAG) depth, i.e., a deeper DODAG negatively impacts the performance of MP2P and P2MP communications, however it positively impacts P2P communication, while the reverse holds true for a relatively shallow DODAG. [ABSTRACT FROM AUTHOR]

An essential building block of today's smart grids is the integration of various renewable power generators into the system. Renewable distributed generators (DGs) provide environment friendly energy at a low cost. In this work, a distributed economic dispatch (ED) system is considered for renewable DGs to address the scalability challenge and reduce single points of failure compared to a centralised ED system. A consensus control mechanism is devised to achieve the incremental cost (IC) consensus among the DGs and utilise a self-triggered control (STC) mechanism to reduce the communication of the distributed DG system. A lossy communication channel between local DGs is considered in this work, which can arise from power lines or congestion on noisy wireless communication channels. A regression-based estimation approach is proposed to account for lost or corrupted data. The proposed method is compared with a centralised ED system and periodic communication. The proposed STC approach achieves the same level of performance as periodic communication while using half the communication resources. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including mobile ad-hoc networks, mobile Internet protocols (IP) and WiMAX.

Online social media provides a channel for monitoring people's social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns. [ABSTRACT FROM AUTHOR]

Industry 5.0 envisions close cooperation between humans and machines requiring ultra-reliable low-latency communications (URLLC). The intelligent reflecting surface (IRS) has the potential to play a crucial role in realizing wireless URLLC for Industry 5.0. IRS is forecast to be a key enabler of 6G wireless communication networks as it can significantly improve wireless network performance by creating a controllable radio environment. In this article, we first provide an over-view of IRS technology and then conceptualize the potential for IRS implementation in a future smart manufacturing environment to support the emergence of Industry 5.0 with a series of applications. Finally, to stimulate future research in this area, we discuss the strength, open challenges, and opportunities of IRS technology in modern smart manufacturing. [ABSTRACT FROM AUTHOR]

This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader–follower approach using linear quadratic regulator control. We study the impact of communication packet loss—containing the position of the leader—on the performance of the presented formation control scheme. The simulation results indicate that packet loss degrades the formation control performance. In order to improve the control performance under packet loss, we propose the use of a long short-term memory neural network to predict the position of the leader by the followers in the event of packet loss. The proposed scheme is compared with two other prediction methods, namely, memory consensus protocol and gated recurrent unit. The simulation results demonstrate the efficiency of the long short-term memory in packet loss compensation in comparison with memory consensus protocol and gated recurrent unit. [ABSTRACT FROM AUTHOR]

Vehicle to vehicle (V2V) communications using dedicated short-range communications (DSRC) are considered a promising technology for enhancing road safety. However, in V2V communications, passenger cars suffer from obstruction of bigger vehicles such as buses or trucks. Based on our measurement, a big vehicle can cause a signal loss from 10 to 15 dB due to shadowing. This results in a shorter communication range and reduces the safety message dissemination capability. In this paper, we analyze the impact of shadowing caused by multiple big vehicles on the V2V communication of passenger cars. We propose a model that takes into account both geometric and stochastic shadowing of multiple big vehicles. In order to cope with the physical size of vehicles and safety distance between them in the real world, we propose to use a repulsive point process called hardcore repulsive Poisson point process (PPP) to model the locations of vehicles. Generating procedure and some basic properties of this process are introduced. Based on the proposed model, we derive the average length of the shadow region and the shadowing loss caused by multiple big vehicles. We show that when the number of big vehicles increases from 10% to 80% of total vehicles in one lane, the shadow region increases from 50 m to 450 m on the road. Furthermore, the shadowing loss causes the relative number of cars within communication range of a typical car to reduce from 90% to between 15% and 50%, depending on whether the big vehicles are in the same or the adjacent lane of the consider car. We analyse the packet collision probability while taking into consideration the big vehicle shadow region and observe significant packet collisions without dedicated bus lanes. [ABSTRACT FROM AUTHOR]

An autonomous V2V communication mode (also known as side-link mode 4), which facilitates V2V communication in out of eNB coverage areas, has recently been introduced into the Long term evolution (LTE) standard. Recent research has studied the performance of this LTE-V2V autonomous mode for a highway use case. However, performance analysis for a highway use case cannot be easily applied to an intersection use case as it may contain non-line-of-sight (NLOS) communication links. In this paper, we analyze and evaluate the safety message broadcasting performance of LTE-V2V autonomous mode in an urban intersection scenario. Considering practical path loss models, we present the impact of NLOS communication link on the overall message dissemination performance. Through the analytical and simulation results, we show that the overall message dissemination performance degrades drastically with increasing vehicle density and increasing distance of the transmitting vehicle from the intersection. To improve the performance, we propose a vehicle-assisted relaying scheme in which the relaying vehicle is selected in an autonomous manner. We also present two resource allocation strategies for the relaying vehicle. For low to medium vehicle density along the street, we observe significant improvement in message dissemination through relaying compared to the scheme without relaying. [ABSTRACT FROM AUTHOR]

Background: While there is recognition of the relationship between loneliness and depression, specific behavioural patterns distinguishing both are still not well understood., Objectives: Our objective is to identify distinct behavioural patterns of loneliness and depression from a passively collected dataset of college students, understand their similarities and interrelationships and assess their effectiveness in identifying loneliness and depression., Methods: Utilizing the StudentLife dataset, we applied regression analysis to determine associations with self-reported loneliness and depression. Mediation analysis interprets the relationship between the two conditions, and machine learning models predict loneliness and depression based on behavioural indicators., Results: Distinct behavioural patterns emerged: high evening screen time (OR = 1.45, p = 0.002) and high overall phone usage (OR = 1.50, p = 0.003) were associated with more loneliness, whereas depression was significantly associated with fewer screen unlocks (OR = 0.75, p = 0.044) and visits to fewer unique places (OR = 0.85, p = 0.023). Longer durations of physical activity (OR = 0.72, p = 0.014) and sleep (OR = 0.46, p = 0.002) are linked to a lower risk of both loneliness and depression. Mediation analysis revealed that loneliness significantly increases the likelihood of depression by 48%. The prediction accuracy of our XGBoost-based machine learning approach was 82.43% for loneliness and 79.43% for depression., Conclusion: Our findings show that high evening screen time and overall phone usage are significantly associated with increased loneliness, while fewer screen unlocks and visits to fewer unique places are significantly related to depression. The findings can help in developing targeted interventions to promote well-being and mental health in students., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Smart manufacturing is a vision and major driver for change in today's industry. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring, controlling, and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communication protocols and their performance to meet application requirements. Particularly, wireless communication is becoming an integral part of modern smart manufacturing and is expected to play an important role in achieving the goals of smart manufacturing. This paper presents an extensive review of wireless communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research in wireless communication technologies for smart manufacturing.

Background: Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation., Objective: This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues., Methods: A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems., Results: After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation., Conclusions: Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns., (©Malik Muhammad Qirtas, Evi Zafeiridi, Dirk Pesch, Eleanor Bantry White. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 12.04.2022.)