Routing Management Research Articles

Design a prototype for automated patient diagnosis in wireless sensor networks.

It is indeed necessary to design of an elderly support mobile healthcare and monitoring system on wireless sensor network (WSN) for dynamic monitoring. It comes from the need for maintenance of healthcare among patients and elderly people that leads to the demand on change in traditional monitoring approaches among chronic disease patients and alert on acute events. In this paper, we propose a new automated patient diagnosis called automated patient diagnosis (AUPA) using ATmega microcontrollers over environmental sensors. AUPA monitors and aggregates data from patients through network connected over web server and mobile network. The scheme supports variable data management and route establishment. Data transfer is established using adaptive route discovery and management approaches. AUPA supports minimizing packet loss and delay, handling erroneous data, and providing optimized decision-making for healthcare support. The performance of AUPA's QoS approach is tested using a set of health-related sensors which gather the patient's data over variable period of time and send from a source to destination AUPA node. Experimental results show that AUPA outperforms the existing schemes, namely SPIN and LEACH, with minimal signal loss rate and a better neighborhood node selection and link selection. It diminishes the jitter compared to the related algorithms. Graphical abstract Stack architecture of AUPA.

Real-time wavelength reuse based on Bragg trans-reflection for dynamic reference frequency and data transfer in Datacom

ABSTRACTWe experimentally demonstrate the first VCSEL-based all-optical wavelength reuse technique with reconfigurable fibre Bragg grating add and drop multiplexer. EDFA gain saturation and Bragg trans-reflection effect on a single FBG are respectively, adopted for full-duplex reference frequency and data transfer. A 1550 nm energy-efficient VCSEL is modulated with 1.7 GHz clock signal and transferred downstream over 26.6 km fibre OLT attain a phase noise stability of −54.01 dBc/Hz at 10 kHz offset frequency. A saturated EDFA is exploited to optically reduce the peak-to-peak voltage of the incoming downstream RF, allowing for wavelength reuse with 10 Gbps upstream data. A 1.57 dB transmission penalty is incurred over the transmission fibre. An all-passive OADM is developed exploiting Bragg trans-reflection at 1549.45 nm. The reflected wavelength is routed over another 24.7 km fibre network attaining an extinction ratio of 6.1 dB and a SNR of 5.8 dB. This work provides an all-optical technique for routing and spectral management in flexible networks.

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

Water for all: Towards an integrated approach to wetland conservation and flood risk reduction in a lowland catchment in Scotland

Strategies for sustainable water resources management require integration of hydrological, ecological and socio-economic concerns. The “Water for all” project has sought to develop a multi-disciplinary science case for innovative management of water levels and flows in a lowland catchment in Scotland. Water demands of arable agriculture, protection from flood risk and conservation needs of lowland mesotrophic wetlands needed to be considered. Water management strategy focused on the outlet zone of Balgavies lake in Eastern Scotland, where the Lunan Water discharges into a partially confined common channel (lade). Water releases to a mill, to the downstream river, and to floodplain wetlands (Chapel Mires) are partially controlled by an existing weir. Based on observations of management of this weir, we postulated that upgrading hydraulic management in this zone could reduce upstream flood risk, help protect mesotrophic wetlands and facilitate downstream water supply at low flows. We considered potential for: (a) installing a remotely operated tilting weir, for improved management of release and routing of flows from the common lade; (b) dredging of the common lade in combination or instead of the tilting weir. Rapid ecological assessment and mixing analysis of the Lunan Water with waters in Chapel Mires showed a gradient of trophic status across the wetlands linked to impact of river-borne nutrients. Stage-discharge relationships, derived from steady-state approximations of the in-channel hydraulics, showed that the proposed tilting weir had potential to divert seasonal nutrient rich water from the upstream Lake away from Chapel Mires. Significant impact of the proposed weir on upstream flood risk was not demonstrated, but carrying out dredging of the channel reduced the current observed probability of upstream flooding. The proposed weir could help to maintain these dredging benefits. Survey and interviews with catchment stakeholders and residents showed constructive interest in the scheme, with half of the respondents willing to pay to support its implementation. The survey also revealed concerns about the proposed project, especially its long-term governance. The lessons learned have wider relevance to development of an integrated approach to water ecosystem services provision, especially where benefits are uncertain and thinly spread across a range of users.

Arch and access vessel complications in penetrating aortic ulcer managed with thoracic endovascular aortic repair.

To analyze our experience and to describe access and arch-related challenges when performing thoracic endovascular aortic repair (TEVAR) for penetrating aortic ulcers (PAUs). This is a single-center, observational, cohort study. Between October 2003 and February 2019, 48 patients with PAU were identified; 37 (77.1%) treated with TEVAR were retrospectively analyzed. Primary major outcomes were early (<30 days) and late survival, freedom from aortic-related mortality (ARM), and a composite endpoint of arch/vascular access-related complications. On admission, 17 (45.9%) patients were symptomatic with 4 (10.8%) presenting with rupture. In-hospital mortality was 8.1% (n=3). We observed 10 (27.0%) arch/access-related complications. There were 4 (10.8%) arch issues: 2 transient ischemic attacks and 2 retrograde acute type A dissections which required emergent open conversion for definitive repair. Access issues occurred in 6 (16.2%) patients: 3 (8.1%) required common iliac artery conduit, and 1 (2.7%) patient required iliac artery angioplasty to deliver the stent-graft. In addition, 2 (5.4%) patients developed access complications which required operative repair [femoral patch angioplasty (n=2), and femoral pseudoaneurysmectomy (n=1)]. Arch/access-related mortality rate was 5.4% (n=2) and median follow-up was 24 (range, 1-156; IQR, 3-52) months. Estimated survival was 87.1% (standard error: 0.6; 95% CI: 71.2-84.9%) at 1 year, and 63.3% (SE: 0.9; 95% CI: 44.1-79%) at 4 years. Estimated freedom from reintervention was 88.9% (SE: 0.5; 95% CI: 74.8-95.6%) at 1 year, and 84.2% (SE: 0.7; 95% CI: 67.3-93.2%) at 4 years. No arch/access-related issues developed during the follow-up period. Our experience confirms that vascular access and aortic arch issues are still a challenging aspect of performing TEVAR for PAUs. Our cumulative 27% rate of access/arch issues is lower than previously reported due to both technological advancements and meticulous management of both access routes and arch anatomy.

Efficacy of Nebulised Tobramycin in Cystic Fibrosis Management: A Systematic Review

Cystic Fibrosis (CF) is a progressive clinical condition associated with decreased functionality of glands that produce mucus, sweat, and intestinal secretions. CF is one of the most common recessive genetic diseases that affect all ethnic groups without any known identified cure, having variations in the severity of symptoms. In the treatment and management of CF, The choice and routes of administration of antibiotics taken is important in assessing effectiveness at different populations. In the light of this, current study was undertaken to determine the efficacy of Nebulised Tobramycin (NT) in the treatment of mild to moderate CF within 8years study period. Study was achieved by searching known repositories and electronic databases of various sources. Randomized Clinical Trials (RCTs) that compares patients treated on NT and placebo were selected, including six (6) RCTs comprising 609 participants to the review. Study noticed significant clinical and methodological heterogeneity among trials with NT, varying over a total of 8years study period. Also, there were recorded evidences of improvement in pulmonary function in most of the patients, following completion of the study. This review revealed inconclusive evidence in the efficacy of NT in the treatment of mild to moderate CF. It is thus clear, that long term use of NT in mild to moderate CF can provide sustained improvements in pulmonary functions.

Identification of Parameters That Predict Sport Climbing Performance.

In recent years, extreme sport-related pursuits including climbing have emerged not only as recreational activities but as competitive sports. Today, sport climbing is a rapidly developing, competitive sport included in the 2020 Olympic Games official program. Given recent developments, the understanding of which factors may influence actual climbing performance becomes critical. The present study aimed at identifying key performance parameters as perceived by experts in predicting actual lead sport climbing performance. Ten male (Mage = 28, SD = 6.6 years) expert climbers (7a+ to 8b on-sight French Rating Scale of Difficulty), who were also registered as climbing coaches, participated in semi-structured interviews. Participants’ responses were subjected to inductive-deductive content analysis. Several performance parameters were identified: passing cruxes, strength and conditioning aspects, interaction with the environment, possessing a good climbing movement repertoire, risk management, route management, mental balance, peer communication, and route preview. Route previewing emerged as critical when it comes to preparing and planning ascents, both cognitively and physically. That is, when optimizing decision making in relation to progressing on the route (ascent strategy forecasting) and when enhancing strategic management in relation to the effort exerted on the route (ascent effort forecasting). Participants described how such planning for the ascent allows them to: select an accurate and comprehensive movement repertoire relative to the specific demands of the route and reject ineffective movements; optimize effective movements; and link different movements upward. As the sport of climbing continues to develop, our findings provide a basis for further research that shall examine further how, each of these performance parameters identified, can most effectively be enhanced and optimized to influence performance positively. In addition, the present study provides a comprehensive view of parameters to consider when planning, designing and delivering holistic and coherent training programs aimed at enhancing climbing performance.

A game-based production operation model for water resource management: An analysis of the South-to-North Water Transfer Project in China

Abstract: To alleviate water shortage, several water transfer projects have been constructed in China. To further develop the supply chain management of water resources, the Eastern and Central Routes of China's South-to-North Water Transfer Project are considered as two water suppliers. The two suppliers provide water for two distributors in northern China. Based on a two-stage Stackelberg game, we develop a chain-to-chain competition model in three alternating scenarios including under wholesale price contracts, two-part pricing contracts and mixed pricing contracts, and derive the optimal pricing and ordering strategies. We find that each water supply chain member can earn a higher profit under the two-part pricing contract with appropriate parameters compared to that under the wholesale price contract. When the competition is weak, the performance of the supply chain subsystem under the two-part pricing contract is better than that under the wholesale pricing contract. On the contrary, when the competition is strong, the performance of the supply chain under the two-part pricing contract is lower than that under the wholesale price contract. With the two-part pricing contract used for both water supply chains, the two suppliers are in the prisoners' dilemma. The study expands the theory of supply chain management under competition and provides insights for the sustainability of water resource management.

ACO-Inspired Energy-Aware Routing Algorithm for Wireless Sensor Networks

Multi-hop networks, such as WSNs, become an object of increasing attention as an emerging technology which plays an important role for practical IoT applications. These multi-hop networks generally consist of mobile and small terminals with limited resources, which makes them vulnerable to various network status changes. Moreover, the limited nature of terminal resources available, especially in terms of battery capacity, is one of the most important issues to be addressed in order to prolong their operating time. In order to ensure efficient communications in such networks, much research has already been conducted, especially in the field of routing and transmission technologies. However, conventional approaches adopted in the routing field still suffer from the so-called energy hole problem, usually caused by unbalanced communication loads existing due to difficulties in adaptive route management. To address this issue, the present paper proposes a novel routing algorithm that utilizes ACO-inspired routing based on residual energy of terminals. Operational evaluation reveals its potential to ensure balanced energy consumption and to boost network performance.

Hybridizing Basic Variable Neighborhood Search With Particle Swarm Optimization for Solving Sustainable Ship Routing and Bunker Management Problem

This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing carbon emissions within the maritime transportation domain remains one of the most significant challenges as it addresses the sustainability aspect. Bunker fuel management deals with the fuel bunkering issues faced by different ships, such as selection of bunkering ports and total bunkered amount at a port. A novel mathematical model is developed capturing the intricacies of the problem. A hybrid particle swarm optimization with a basic variable neighborhood search algorithm is proposed to solve the model and compared with the exact solutions obtained using Cplex and other popular algorithms for several problem instances. The proposed algorithm outperforms other popular algorithms in all the instances in terms of the solution quality and provides good quality solutions with an average cost deviation of 5.99% from the optimal solution.

Double-Head Clustering for Resilient VANETs

Scalability and the highly dynamic topology of Vehicular Ad Hoc Networks (VANETs) are the biggest challenges that slow the roll-out of such a promising technology. Adopting an effective VANET clustering algorithm can tackle these issues in addition to benefiting routing, security and media access management. In this paper, we propose a general-purpose resilient double-head clustering (DHC) algorithm for VANET. Our proposed approach is a mobility-based clustering algorithm that exploits the most relevant mobility metrics such as vehicle speed, position, and direction, in addition to other metrics related to the communication link quality such as the link expiration time (LET) and the signal-to-noise ratio (SNR). The proposed algorithm has enhanced performance and stability features, especially during the cluster maintenance phase, through a set of procedures developed to achieve these objectives. An extensive evaluation methodology is followed to validate DHC and compare its performance with another algorithm using different existing and newly proposed evaluation metrics. These metrics are analyzed under various mobility scenarios, vehicle densities, and radio channel models such as log-normal shadowing and two-ray ground loss with and without Nakagami-m fading model. The proposed algorithm DHC has proven its ability to be more stable and efficient under different simulation scenarios.

Securing Emergency Communications for the Next-Generation Networks

Delay Tolerant Network (DTN) provides connectivity where there is uncertainty in end-to-end connectivity. In DTN, nodes exchange buffered messages upon an encounter. In disaster operations where the telecommunication and power infrastructures are completely broken down or destroyed, DTN can be used to support emergency communication till these infrastructures are restored. Security in DTN remains a major challenge because of its network characteristics such as frequent disruptions, dynamic topology, limited and constrained resources. One of the major threats in DTN is Denial of Service (DoS) attacks. This attack mainly comes from intermediary nodes that drop or flood packets in the network which often results in the degradation of the network performance. In this paper, we propose a collaborative content-aware trust forwarding for emergency communication networks. Extensive simulations and validations show that the proposed schemes outperform existing routing and trust management protocols in the presence of malicious nodes and are resilient to trust related attacks.

Optimal routing for automated emergency vehicle response for incident intervention in a traffic network

Congestion constitutes a major problem in modern urban traffic networks if not well managed. Its monstrous effects, on occasions, can paralyze a traffic network eating deep into the productive hours of commuters as well as vehicles and persons on essential services. Particularly affected are incidence-intervention vehicles such as emergency vehicles and fire-fighting vehicles. Whatever the cause of the congestion, its effect is counter-productive and an indication of an inefficient traffic network. This work, as presented in this paper, is concerned about the issue of traffic route management for emergency service (emergency vehicle) for which a delay of few minutes may cause tremendous loss of lives and properties. The route management scheme built for this purpose integrates information obtained from the use of Radio Frequency Signals for Traffic Light Preemption at Intersections in a Proteus Simulator environment and the use Arc GIS as a mode of routing the emergency vehicle from base to the incidence location, then to Health Facilities and from thence back to the emergency vehicle base in an optimal routing time. Traffic information are loaded into the Arc GIS environment which predicts the required tri-legged optimal routing and its duration using Dijkstra’s algorithm. Different scenarios of emergency vehicle, incidence and health facility locations were exploited using the scheme and compared with situations without their implementation. The proposed scheme outperforms the trial and error routing of emergency vehicles and can be embedded into traffic advisory system or as stand-alone emergency vehicle management system. Keywords: GIS, Dijkstra’s algorithm, Facility Location, Emergency-Vehicle, Optimal Routing

Long-Term Monitoring of the Atlantic Jet through the Strait of Gibraltar with HF Radar Observations

The present work focuses on the long-term coastal monitoring of the Atlantic surface inflow into the Mediterranean basin through the Strait of Gibraltar. Hourly current maps provided during 2016–2017 by a High Frequency radar (HFR) system were used to characterize the Atlantic Jet (AJ) since changes in its speed and direction modulate the upper-layer circulation of the Western Alboran Gyre (WAG). The AJ pattern was observed to follow a marked seasonal cycle. A stronger AJ flowed north-eastwards during autumn and winter, while a weaker AJ was directed more southwardly during the middle of the year, reaching a minimum of intensity during summertime. A strong relationship between AJ speeds and angles was evidenced: the AJ appeared to be frequently locked at an angle around 63°, measured clockwise from the North. The AJ speed usually fluctuated between 50 cm·s−1 and 170 cm·s−1, with occasional drops below 50 cm·s−1 which were coincident with abrupt modifications in AJ orientation. Peaks of current speed clearly reached values up to 250 cm·s−1, regardless of the season. A number of persistent full reversal episodes of the surface inflow were analyzed in terms of triggering synoptic conditions and the related wind-driven circulation patterns. High sea level pressures and intense (above 10 m·s−1), permanent and spatially-uniform easterlies prevailed over the study domain during the AJ collapse events analyzed. By contrast, tides seemed to play a secondary role by partially speeding up or slowing down the westward currents, depending on the phase of the tide. A detailed characterization of this unusual phenomenon in the Strait of Gibraltar is relevant from diverse aspects, encompassing search and rescue operations, the management of accidental marine pollution episodes or efficient ship routing.

O2O on-demand delivery optimization with mixed driver forces

O2O (Online to Offline) service enables customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organize the offline delivery. In practice, three types of workforce, namely, in-house drivers, full-time and part-time crowd-sourced drivers, coexist in the system with different characteristics. This posts challenges to the management of order assignment and routing by the online platform. In particular, we study the impact of the detour flexibility for part-time crowd-sourced drivers, which affects the participation rate of this type of workforce. This paper aims to provide a systematic method for the O2O platforms to optimize order assignment and routing. We further validate our model and study the managerial insights using real datasets from one of the mainstream O2O platforms in China.

Augmented reality use for cycling quality improvement

The impact of air pollution on the health of the population and the greenhouse effect determine the development of green transport modes. One is cycling. The developed a dual-use cycling route planning and management tool VeloRouter provides agent-based routing load simulation and allows each user to choose the most attractive travel time. However, during the trip, the cyclists want to get real-time information not only about the weather, but also about the terrain that would improve travel safety. One of the possible solutions is augmented reality (AR) smart glasses. It is important to develop an appropriate and sustainable AR solution, which is not possible without careful prior analysis.

Modelling and performance analysis of smart waste collection system: a Petri nets and discrete event simulation approach

With the recent development of smart cities all over the world, many new performance services have been developed, including smart mobility, energy and waste collection. In general, waste collection operators face the challenge of routing management, so as to reduce the costs of transportation and energy consumption. This paper proposes a waste collection strategy model based on real time inventory control of the vehicles' routing problem, with stochastic Petri nets (PN). The developed model and simulations show the capability of using PN models to predict the critical situations, analyse collection strategy efficiency, and improve performance. Results from the numerical example indicate that the overall vehicle travel distance for waste collection has been significantly reduced by estimating the exact moment to launch the collection service, as well as resolves conflict between vehicles during the collection. Simulation scenarios were introduced to assess the real time impact by monitoring the collection process.

Route Management for Vehicles Used in Road Maintenance Activities through Hierarchical Chinese Postman Problem Approach

Ayrıt rotalama problemleri, kombinatoriyel optimizasyon problemlerinden biridir.Bu problemlerin amacı, bir şebeke üzerinde yer alan ayrıtların tümünü ya da alt kümelerinikapsayacak şekilde en kısa maliyetli turları bulmaktır. Hiyerarşik Çinli Postacı Problemi(HÇPP), en yaygın ayrıt rotalama problemlerinden biri olan Çinli Postacı Probleminin birtürü olup gerçek hayatta; kar küreme, çöp toplama, yol bakım çalışmaları, mektup dağıtımı vedevriye gezen polis/güvenlik araçlarının rotalanması gibi pek çok uygulama alanı vardır. Buçalışmada, HÇPP yaklaşımı ile Karayolları 12. Bölge Müdürlüğüne bağlı yollarda yapılan bakımçalışmalarının en az maliyetle gerçekleştirilmesi için eniyi/eniyiye yakın rotaların bulunmasıamaçlanmıştır. Ele alınan problem büyük boyutlu olup çözümü için en yakın komşu aramatabanlı bir sezgisel algoritma geliştirilmiştir. Geliştirilen algoritma, ele alınan tüm karayoluşebekesi için çalıştırılmış ve etkin bir sonuç elde edilmiştir.

The design of an IoT-GIS platform for performing automated analytical tasks

Society has a very ambitious vision of building smart interconnected cities through the Internet of Things (IoT). Billions of data streams will be generated by devices using different networking infrastructures of smart cities, enabling the automation of how the data that are being collected can be analysed for. However, significant scientific and technological challenges need to be overcome before IoT-GIS platforms can be widely used. This paper is a first step towards designing an IoT-GIS platform for performing automated analytical tasks that are able to retrieve, integrate and contextualize data streams with the purpose of adding value to the provision of transit services. Three automated tasks are used to describe our platform: (1) data ingestion for retrieving data streams; (2) data cleaning for handling missing and redundant data streams; and (3) data contextualization for representing the mobility context of transit driving behaviour. The Codiac Transit System of the Greater Moncton area, NB, Canada was used for building a mobility context and evaluating the cloud architecture that was used to implement our IoT-GIS platform. From the experimental results, the need for cloud computing for achieving scalability and high performance of our IoT-GIS platform is validated. Suggestions for the operational management of routes to improve service quality are proposed based on the analytical outcomes.

An Efficient Route Management Framework for Load Balance and Overhead Reduction in SDN-Based Data Center Networks

A data center network (DCN) is composed of many servers interconnected by well-organized switches, which involves massive amount of data transmissions to provide cloud services. It is critical to manage packet routes in DCNs to avoid congestion on some links. The software-defined networking (SDN) technique supports auto-configuration of switches by a central controller, and gives an easy way to manage traffic flows. In the paper, we exploit SDN to improve performance of fat-tree DCNs, and propose a low-cost, load-balanced route management (L2RM) framework. L2RM keeps monitoring network traffics and computes a load-deviation parameter to check if some links of switches are burden with heavy loads. Then, an adaptive route modification mechanism is triggered if necessary, which considers the size limitation of flow tables and uses group tables in OpenFlow to distribute flows among different links to balance their loads. Besides, L2RM uses a dynamic information polling mechanism to query switches about their statuses, so as to reduce the message overhead of the controller. Through Mininet simulations, we show that our L2RM framework can better increase link utilization, alleviate table overflow, and reduce message cost, as comparing with other SDN-based approaches for fat-tree DCNs.