Demands Of Nodes Research Articles

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.

An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks

Underwater Internet-of-Things (UIoT) is an extension of Internet-of-Things technology in underwater. The underwater acoustic network with WiFi architecture (UW-WiFi), as a specific deployment of UIoT, has been proved to be a promising technique for wide-ranging marine applications. However, due to the unique features of underwater acoustic channel, such as long and variable propagation delay, low available bandwidth and high bit error rate, conventional medium access control (MAC) protocols designed for terrestrial WiFi networks need an overhaul to work efficiently for UW-WiFi networks. In consideration of the aforementioned channel features, different demands of nodes to occupy channel resources and the reliability of data transmission, a time sequence-based dynamic on-demand assignment (SDDA) MAC protocol for UW-WiFi networks is proposed in this paper. In SDDA, the collision-free scheduling is integrated with the reservation mechanism, aiming to address the issue of various access requirements of diverse task nodes on channel resources in the UW-WiFi network system. The designed protocol employs propagation delays and the amount of data to be sent by terminal nodes to achieve non-conflict transmissions of control packets and on-demand scheduling of data packets. Additionally, the scheme does not require extra overhead for time synchronization. Comparison simulations demonstrate that SDDA provides considerable benefits in terms of channel utilization, end-to-end delay and packet delivery ratio. At last, the SDDA protocol is implemented and a UW-WiFi network system is set up in the marine environment. The ocean field experiment results agree well with the simulated ones and also verify that the proposed protocol achieve conflict-free on-demand scheduling, fairness among terminal nodes at different ranges and the practicability in actual environments.

Application of multi-agent decision-making methods in hydrological ecosystem services management

In this paper, a methodology is presented for managing hydrological ecosystem services by taking into account the hierarchy of stakeholders involved in the decision-making process. With this in mind, a water allocation model is first used for allocating water resources to demands. Then, several ecosystem services (ESs)-based criteria are defined to evaluate hydrological ESs of water resources management policies. A set of water and environmental resources management strategies (alternatives) are defined for decision-makers, and several drought management strategies are determined to decrease the area of key crops and water demands of agricultural nodes. To model a multi-agent multi-criteria decision-making problem for managing hydrological ESs, three main steps are considered as follows:•Different ES-based criteria (i.e., economic profit, NPP,11Net Primary Productivity and ecological index) are defined, and their grade-based values are estimated.•Several strategies are defined for stakeholders at different levels.•A recursive evidential reasoning (ER) approach, which considers a hierarchical structure for decision-makers and a leader-follower game, is used to select the best strategy for each decision-maker.The applicability and efficiency of the methodology are illustrated by applying it to a real-world case study. The methodology is general and can be easily applied to other study areas.

Uncertainty analysis of water quality in water distribution system

Abstract Water quality simulation is affected by uncertain parameters such as pipe roughness coefficients, chlorine bulk decay coefficients, and chlorine wall decay coefficients, which are usually considered to be fuzzy variables. The minimum and maximum nodal chlorine concentrations and water ages at each α-cut level were obtained by the genetic algorithm (GA) based on EPANET hydraulic and water quality simulation toolkit. The fuzziness of nodal chlorine concentrations and water ages were measured using the fuzziness measure (FM) proposed in this paper. The method was applied to four networks to analyze the fuzziness of nodal chlorine concentrations and water ages. The results indicated that the distribution of nodal chlorine concentrations does not follow typical trapezoid distribution, while the distribution of nodal water ages follows typical trapezoid distribution. In addition, the chlorine concentration and water ages of nodes farther from the source are affected by uncertain parameters to a greater extent. The greater demands of nodes lead to less effects of uncertain parameters on chlorine concentration, and greater effects of uncertain parameters on water ages. This study would help in analyzing the fuzziness of hydraulic and water quality simulation results in WDS under uncertain conditions.

Probabilistic cooperative coded forwarding for broadcast transmissions in industrial mobile edge communications

Mobile edge computing (MEC) is considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited resource of the IIoT nodes, it is necessary to design cost-effective multi-hop data transmission schemes for mobile edge IIoTs. However, most of the traditional schemes have to spend enormous cost to meet the reliability requirement, which cannot support timely information processing of MEC-based IIoT. In this article, the probabilistic cooperative coded forwarding (PCCF) scheme for multi-hop data transmission in mobile edge IIoTs is proposed to address the above problem. First, the data packets are encoded at a source IIoT node using the systematic sparse network coding (SSNC) mechanism, then the source broadcasts the coded packets to its one-hop neighbors. To minimize the required number of redundant coded packets, the sparsity of coded packets is optimized. Second, the nodes which received the packets will become volunteer relay nodes and forward the coded packets using the cooperative coded forwarding (CCF) mechanism. The volunteer relays first forward the received coded packets with a forwarding probability, and then re-encode a pair of received coded packets and broadcast the re-encoded packet with a re-encoding probability. To guarantee the broadcast performance while minimizing the transmission number at relay nodes, the feasible forwarding and re-encoding probability are provided. Third, the receiver nodes will try to decode the received coded and re-encoded packets and recover data packets without sending acknowledgments. Finally, through a series of experiments, we verify the accuracy of analytical approximations and also find out the optimal sparsity of coded packets and the existence of minimum transmission numbers. These provide insights for further optimization of multi-hop data transmission in mobile edge IIoTs.

Enabling Sustainable Underwater IoT Networks With Energy Harvesting: A Decentralized Reinforcement Learning Approach

In this article, we study an energy sustainable Internet-of-Underwater Things (IoUT) network with tidal energy harvesting. Specifically, an analytical model is first developed to analyze the performance of the IoUT network, characterizing the stochastic nature of energy harvesting and traffic demands of IoUT nodes, and the salient features of acoustic communication channels. It is found that the spatial uncertainty resulting from underwater acoustic communication may cause a severe fairness issue. As such, an optimization problem is formulated to maximize the network throughput under fairness constraints, by tuning the random access parameters of each node. Given the global network information, including the number of nodes, energy harvesting rates, communication distances, etc., the optimization problem can be efficiently solved with the Branch and Bound (BnB) method. Considering a realistic network where the network information may not be available at the IoUT nodes, we further propose a multiagent reinforcement learning approach for each node to autonomously adapt the random access parameter based on the interactions with the dynamic network environment. The numerical results show that the proposed learning algorithm greatly improves the throughput performance compared with the existing solutions, and approaches the derived theoretical bound.

A route decomposition approach for the single commodity Split Pickup and Split Delivery Vehicle Routing Problem

We address a single commodity Pickup and Delivery Vehicle Routing Problem from the literature. A network of customer nodes is given with both travel times and costs. A fleet of vehicles of limited capacity is exploited to satisfy node demands, and a set of routes must be found such that the vehicle capacities are never exceeded, each route does not exceed time resource, and cost is minimized. The demands of both pickup and delivery nodes can be split, and each node can be visited more than once. We provide new theoretical insights. We propose a new formulation where routes are decomposed into sequences of simpler substructures called clusters, mitigating the combinatorial explosion of feasible solutions. We introduce valid inequalities, and design a branch-and-price algorithm, exploiting ad hoc pricing routines and branching strategies, and embedding a rounding heuristic to speed up pruning. An extensive experimental analysis shows our method to offer simultaneously more modelling flexibility and more computational effectiveness than previous attempts from the literature. Our investigation opens also interesting insights into the use of route decomposition strategies.

Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints

This paper proposes a subnetwork origin-destination (OD) matrix estimation model under travel demand constraints (SME-DC) that explicitly considers both internal-external subnetwork connections and OD demand consistency between the subnetwork and full network. This new model uses the maximum entropy of OD demands as the objective function and uses the total traffic generations (attractions) along with some fixed OD demands of the subnetwork OD nodes as the constraints. The total traffic generations and attractions along with the fixed OD demands of the subnetwork OD nodes are obtained through an OD node transformation and subnetwork topology analysis. For solving the proposed model, a convex combination method is used to convert the nonlinear SME-DC to the classical linear transportation problem, and a tabular method is used to solve the transportation problem. The Sioux Falls network and Kunshan network were provided to illustrate the essential ideas of the proposed model and the applicability of the proposed solution algorithm.

A new multi-objective model of agile supply chain network design considering transportation limits

ABSTRACTIn this research, a triple-objective model with the assumption of multi-material and multi-product in five stage of supply chain network (SCN) is introduced. A five-stage supply chain network includes the following: supplier, manufacturer, retailer and distributor as well as customer zone. The goal is to determine production and supply quantity in each node of supply chain network under stochastic demand of a competitive market. The first objective is minimizing the expenses such as transportation, inventory, shortage and production. The second objective is minimizing the risks in each node as well as route of supply chain network which means considering customer risk in each customer zone and making a connection with supplier zone regarding the SCN routs. The third objective is maximizing flexibility which leads to a restraint model in fluctuation of the market demand. Having the constraints in mind, transportation volume, transportation speed and transportation vehicle are taken into account. The capacity of production is considered with respect to the demands of nodes in the next stage. The inventory and shortage are considered as the constraints of the step. In the final step, a network with different number of nodes is designed which brings about the use of modified NSGA-II regarding the multi-encoding. The results display flexibility effect in supply chain network which dominates the uncertainty situation of the market and a better restraining approach for SCN.

A branch and price approach for the Split Pickup and Split Delivery VRP

In this paper we address a Pickup and Delivery Vehicle Routing Problem where the demands of both pickup and delivery nodes can be split between several vehicles. We investigate a new formulation where the route of each vehicle is decomposed into a sequence of simpler substructures called clusters, mitigating the combinatorial explosion of feasible solutions. We implement a branch-and-price algorithm exploiting column generation procedures that dynamically generates clusters to obtain improved dual bounds, and ad hoc branching strategies to achieve integrality.

Reliability of nonrepairable phased-mission systems with common bus performance sharing

This article considers the reliability analysis of phased-mission systems with common bus performance sharing. The whole system consists of client nodes, service elements, and a common bus redistribution system and it undertakes a multi-phase mission. In each phase, the service elements must satisfy the demands of the prespecified client nodes set. The service elements can share their surplus performance with other client nodes through the common bus. In any phase, the system fails if the demands of the prespecified client nodes set are not satisfied. In other words, the entire system succeeds if the demands of the prespecified client nodes set are satisfied in all phases. The reliability of the proposed model is analyzed by the backward recursive algorithm. The optimal allocation problem is solved by the genetic algorithm. Two examples are presented to demonstrate the proposed reliability evaluation method and optimal allocation algorithm.

Joint Bandwidth Optimization and Media Access Control for Multihop Underwater Acoustic Sensor Networks

Multihop underwater acoustic sensor networks (UASNs) suffer from low throughput caused by low channel utilization and unreasonable bandwidth allocation. The existing media access control (MAC) protocols for UASNs mainly aim to improve channel utilization but neglect to optimize bandwidth, so that they cannot achieve high throughput in miltihop networks. We propose the idea of joint bandwidth optimization and MAC in this paper. We first present a system model to optimize bandwidth for miltihop UASNs. Both the complicated interference relations between links and the different transmission demands of nodes are modeled. Thus, it can accurately reflect the characteristics of miltihop UASNs. By analyzing a 1-D linear miltihop network, we find and formulate a maximum hop number which is determined by nodes' traffic load and channel capacity. The other finding is that an optimal scheduling needs to allocate the bandwidth based on traffic load, and the nodes with more traffic load should be allocated more bandwidth. This finding inspire us to develop a distributed traffic-based scheduling MAC protocol. This protocol can optimize bandwidth allocation by letting the older packet to be transmitted preferentially. To improve channel utilization, multiple packets are allowed to be transmitted in each data transmission round. Simulation results have confirmed that our protocol can achieve excellent performance owing to its core idea of joint bandwidth optimization and MAC.

Development of a self-contained wireless-based SHM system for monitoring a swing bridge

Structural health monitoring (SHM) based on wireless sensor network (WSN) technology has been introduced into civil infrastructures in recent years due to its advantages of easy and flexible installation, low system cost and increased robustness. The wireless–based SHM system involves a multidisciplinary effort that encompasses a number of synergetic technologies brought together to provide a system that can potentially monitor the structural condition of a structure in real time. In this paper, a WSN for SHM is designed, implemented and tested on a swing Beaufort #25 Bridge at the East Coast of North Carolina. To characterise the dynamic behaviour of the swing bridge, seven wireless sensor nodes are distributed along the 55–m main span, which intended to collect acceleration data from the structure under ambient vibration. The acquisition data will then be stored and sent via the base station to a website, where the user can download these data and display it by a GUI tool. The wireless sensing unit is designed not only for reliable communication of response acceleration measurements but also for self–powered capacity. This study explores two energy harvesting approaches, solar panel and miniature wind turbine, to provide the power demands of the wireless sensor nodes.

Traffic-aware channelization medium access control for wireless ad hoc networks

Existing multi-channel Medium Access Control (MAC) protocols have been demonstrated to significantly increase wireless network performance compared to single channel MAC protocols. Traditionally, the channelization structure in IEEE 802.11 based wireless networks is pre-configured, and the entire available spectrum is divided into subchannels and equal channel widths. In contrast, this paper presents a Traffic-Aware Channelization MAC (TAC-MAC) protocol for wireless ad hoc networks, where each node is equipped with a single half duplex transceiver. TAC-MAC works in a distributed, fine-grained manner, which dynamically divides variable-width subchannels and allocates subchannel width based on the Orthogonal Frequency Division Multiplexing (OFDM) technique according to the traffic demands of nodes. Simulations show that the TAC-MAC can significantly improve network throughput and reduce packet delay compared with both fixed-width multi-channel MAC and single channel 802.11 protocols, which illustrates a new paradigm for high-efficient multi-channel MAC design in wireless ad hoc networks.

QoS-based cooperative algorithm for integral multi-commodity flow problem

This paper treats with integral multi-commodity flow through a network. To enhance the Quality of Service (QoS) for channels, it is necessary to minimize delay and congestion. Decreasing the end-to-end delay and consumption of bandwidth across channels are dependent and may be considered in very complex mathematical equations. To capture with this problem, a multi-commodity flow model is introduced whose targets are minimizing delay and congestion in one model. The flow through the network such as packets, also needs to get integral values. A model covering these concepts, is NP-hard while it is very important to find transmission strategies in real-time. For this aim, we extend a cooperative algorithm including traditional mathematical programming such as path enumeration and a meta-heuristic algorithm such as genetic algorithm. To find integral solution satisfying demands of nodes, we generalize a hybrid genetic algorithm to assign the integral commodities where they are needed. In this hybrid algorithm, we use feasible encoding and try to keep feasibility of chromosomes over iterations. By considering some random networks, we show that the proposed algorithm yields reasonable results in a few number of iterations. Also, because this algorithm can be applied in a wide range of objective functions in terms of delay and congestion, it is possible to find some routs for each commodity with high QoS. Due to these outcomes, the presented model and algorithm can be utilized in a variety of application in computer networks and transportation systems to decrease the congestion and increase the usage of channels.

The Transport-Production Coordination Problem

Vehicle route planning process, which follows a transportation problem solution, often provides a system of cyclic routes of vehicles. This system should cover transportation demands of individual network nodes within a middle term period. Due to non-uniform demand distribution over the period, a new problem emerges in particular parts of the period. This problem consists in balancing the sub-period demands and frequencies, with which the individual routes are performed. This new problem denoted as the transport-production coordination problem is studied in this contribution.We described the problem by mathematical programming tools and proposed a decomposition method to enable the problem decomposition to irreducible sub-problems. The individual types of the problem were studied and particular solving algorithms were designed. To complete our approach to this NP-hard problem, we developed an exchange heuristic and explored its properties. The associated computational study was performed with several series of the irreducible sub-problems, which were solved by the exchange heuristic and, simultaneously, they were solved to optimality by the universal optimization environment Mosel. The results were used for evaluation of the heuristic quality and time consumption.