Multiple Sinks Research Articles

Sustainable energy-water-food nexus integration and carbon management in eco-industrial parks

Today, resource depletion is a significant threat to society, exacerbated by the increasing demand for water, energy, and food resources driven by rapid population growth. Coupled with environmental conservation efforts, designing optimised and integrated Energy-Water-Food-Carbon nexus networks is increasingly essential. The objective of this study is to design a systematic approach to study the integration of the Energy-Water-Food-Carbon nexus within eco-industrial parks, uniquely focusing on the combination of the Energy-Water-Food nexus concept, constituting technology sub-systems, and carbon capture/utilisation processes. The resulting network superstructure representative of an industrial park and a case study in Qatar, consists of multiple sources and sinks, including, chemical processes (gas-to-liquid, methanol, ammonia, and liquified natural gas), desalination, wastewater treatment, representative food processes, biomass gasification, waste heat recovery and carbon capture units. Multiple scenarios are simulated and solved using the “what's Best” Mixed-Integer Global Solver to capture the synergic potential and trade-offs within resource management. The outcomes determine that the biomass driven and carbon capture utilisation scenarios are the most significant, which improve all emissions by 11.4% and 40%, respectively, while there was no significant change in the scenario of wastewater treatment and reuse. The total cost of the optimum solution after biomass utilisation is almost 3% more expensive compared to the base scenario. Furthermore, the waste heat utilisation scenario can potentially reduce a corresponding global warming potential amount of almost 1.55 × 104 Mt CO2-eq/yr for more efficient resource management.

A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city

The Internet of Things (IoT) is a revolutionary development that facilitates the instant communication of data among devices, making it an essential paradigm in today's world. It enables the development of smart infrastructure and provides assistance in emergency services during disasters. However, there is a growing need to develop energy-conserving paradigms that cover all aspects of a smart city. In this work, the authors present a hybrid genetic algorithm (GA) inspired greedy strategy-based mutation operation for IoT-enabled heterogeneous wireless sensor networks (WSNs) distributed architecture in a technology driven city. The proposed protocol aims to design a weighted fitness function that considers four parameters: node density, residual and average energy, and distance parameters. Additionally, a 3-tier heterogeneity is considered to prolong the network functional duration, and a deployment strategy is addressed to position these nodes in an energy-efficient manner. The proposed algorithm achieves 31.41%, 20.42%, 13.37%, 1.59% increase in network lifespan compared to GA-based optimized clustering (GAOC), optimized GA with single sink (OptiGAS-StSS), multiple sinks (MS) based GAOC and optimized GA with multiple sink (OptiGAS-StMS), respectively. The significant enhancement in network lifespan is an outcome of choosing the most efficient CH nodes based on the proposed fitness function. Overall, the proposed protocol contributes to developing an energy-conserving paradigm for a smart city.

From tree to forest: Multiple carbon sink constraints

From tree to forest: Multiple carbon sink constraints

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

SDAA: Secure Data Aggregation and Authentication Using Multiple Sinks in Cluster-Based Underwater Vehicular Wireless Sensor Network.

Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods.

MMSCM: A multiple mobile sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm

Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.

An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs

Wireless sensor networks consist of many restrictive sensor nodes with limited abilities, including limited power, low bandwidth and battery, small storage space, and limited computational capacity. Sensor nodes produce massive amounts of data that are then collected and transferred to the sink via single or multihop pathways. Since the nodes’ abilities are limited, ineffective data transmission across the nodes makes the network unstable due to the rising data transmission delay and the high consumption of energy. Furthermore, sink location and sensor-to-sink routing significantly impact network performance. Although there are suggested solutions for this challenge, they suffer from low-lifetime networks, high energy consumption, and data transmission delay. Based on these constrained capacities, clustering is a promising technique for reducing the energy use of wireless sensor networks, thus improving their performance. This paper models the problem of multiple sink deployment and sensor-to-sink routing using the clustering technique to extend the lifetime of wireless sensor networks. The proposed model determines the sink placements and the most effective way to transmit data from sensor nodes to the sink. First, we propose an improved ant clustering algorithm to group nodes, and we select the cluster head based on the chance of picking factor. Second, we assign nodes to sinks that are designated as data collectors. Third, we provide optimal paths for nodes to relay the data to the sink by maximizing the network’s lifetime and improving data flow. The results of simulation on a real network dataset demonstrate that our proposal outperforms the existing state-of-the-art approaches in terms of energy consumption, network lifetime, data transmission delay, and scalability.

Multiple Phase Change Material-Based Heat Sink for Cooling of Electronics: A Combined Experimental and Numerical Study

Abstract This paper reports an investigation of the thermal performance of an energy storage heat sink incorporated with multiple phase change materials (PCMs). A six-cavity cylindrical heat sink heated at the base is chosen for the investigations with Docosane, n-Eicosane, and Tetracosane as candidate PCMs. The phase transition of PCMs has been visualized with a digital camera and three-dimensional numerical simulations. The results show that the latent heat exploitation process of PCMs in a heat sink with multiple PCMs is different from the single PCM heat sink, where the PCMs in all cavities melt distinctly rather at a time, thereby opening up windows for obtaining deeper insights that can lead to better performing heat sinks. A trained artificial neural network (ANN) with 78 representative heat sink configurations based on the arrangement of the PCMs in the cavities as input and charging and discharging times as output is used to swiftly drive the optimization engine. Finally, multi-objective optimization is performed using the artificial bee colony algorithm with simultaneous consideration of two conflicting objectives (i.e., maximizing charging cycle time and minimizing discharging cycle time) of the heat sink. From the optimization study, best performing nondominated Pareto optimal heat sink configurations are obtained and validated with the in-house experimental results. From the investigations, it is found that the heat sink configurations with multiple PCMs perform on par with the single PCMs in the charging process and show a superiority of up to 24% in discharging process over a heat sink with single PCMs in terms of time to reach set point temperature.

Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs

In today’s information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.

Enhanced network lifetime in WBAN using hybrid meta-heuristic-enabled mobile and multiple sink nodes-connected routing

Enhanced network lifetime in WBAN using hybrid meta-heuristic-enabled mobile and multiple sink nodes-connected routing

Smart Hybridized Routing Protocol for Animal Monitoring and Tracking Applications

Wireless sensor networks (WSN) have been exploited for {countless} application domains, most notably the surveillance of environments and habitats, which has already become a critical mission. As a result, WSNs have been implemented to monitor animal care and track their health status. However, excessive energy utilization and communication traffic on packet transmissions lead to system deterioration, especially whenever perceived information captured in the monitoring area is transferred to the access point over multiple dynamic sinks. Further to manage the energy and data transmission issue, the energy consumption and location aware routing protocol has been architected on the wireless Nano sensor nodes. In this article, a novel hybrid energy and location aware routing protocol to cloud enabled IoT based Wireless Sensor Network towards animal health monitoring and tracking has been proposed. However proposed data routing protocol incorporates the trace file for path selection for data transmission to base station using sink node. Trace file has been obtained on processing the cluster heads established in the network. Therefore, clustering of node in the network has to be achieved using LEACH protocol which enhances the network scalability and network lifetime by clustering the nodes with Metaheuristics constraints like location or node density comparability. The objective of the proposed model is to enhance the network scalability and energy consumption by establishing the multiple node clusters with high density cluster head through Metaheuristics Node Clustering optimization techniques. Metaheuristics based node clustering is been obtained using Improved Particle Swarm Optimization. Further it is employed to compute the optimal path for sensed data transmission to base station. Node clustering provides high energy consumption among the sensing nodes and to establish the high energy clusters towards sensed information dissemination to base station on dynamically reforming the nodes clusters with respect to Node density and node location. Simulation analysis of the proposed energy efficient routing protocol provides high performance in energy utilization, packet delivery ratio, packet loss and Average delay compared against the conventional protocols on propagation of the data through sink node to base station

Reinforcement learning movement path for multiple mobile sinks in wireless sensor networks

SummaryMobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end‐to‐end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time. Thereupon, WSN routing can improve very much in terms of end‐to‐end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS‐3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark.

Origin, evolution, and future of isoprene and nitric oxide interactions within leaves.

Photolytic generation of nitric oxide (NO), isoprene, and reactive oxygen species (ROS) pre-dated life on Earth (~4 billion years ago). However, isoprene-ROS-NO interactions became relevant to climate chemistry ~50 million years ago, after aquatic and terrestrial ecosystems became dominated by isoprene-emitting diatoms and angiosperms. Today, NO and NO2 (together referred to as NOx) are dangerous biogenic gaseous atmospheric pollutants. In plants, NO, with its multiple sources and sinks, acts as a secondary messenger that regulates development at low doses and induces cell death at high doses. Likewise, biogenic isoprene is a putative antioxidant and hormone 'enabler' that hastens plant (and leaf) growth and reproduction, and improves plant tolerance to transient abiotic stresses. Using examples from controlled-chamber simulation and field studies of isoprene oxidation, we discuss the likely nature and extent of isoprene oxidation within leaves. We argue that isoprene-NO interactions vary greatly among plant species, driven by differences in isoprene emission rate and nitrate assimilation capacity (i.e. NO sink strength), ROS availability, and the within-leaf ratio between free-NO and isoprene. In a warmer and CO2-fertilized future climate, antagonism between isoprene and NO within leaves will probably occur in a NO-rich (relative to present) environment, yielding a greater proportion of isoprene oxidation products, and inducing major changes in NO-mediated growth and stress responses.

An Improved Physical Model Considering Mirco-Nano Scale Effects for Numerical Simulation of Pirani Vacuum Gauges

An improved physical model was proposed for accurate electrothermal modeling Pirani vacuum gauges (PVG) by accounting for the micro-nano scale effects of heat transfer in sensitive elements and the non-negligible corner heat dissipation from multiple heat sinks. This model is verified by published experimental data and can reveal the influences of geometrical, physical and process parameters, such as device dimensional size, temperature and doping concentrations, on the micro-nano scale heat transfer process, which is a great consideration for microelectromechanical systems (MEMS) PVG designing. Although the model is initially envisaged for MEMS PVG, it could be straightforwardly adopted for the thermal sensors with silicon heaters. Finally, the characteristics of PVG were comprehensively investigated based on the presented model validated by the experimental data. The results show that expanding the area ratio of heat sinks to heater and the thermal resistance ratio of heater to gas is an effective design idea to optimise the maximum sensitivity and central pressure of device.

Multiobjective Multiple Mobile Sink Scheduling via Evolutionary Fuzzy Rough Neural Network for Wireless Sensor Networks

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.

Dynamic Behavior and Vibration Suppression of a Generally Restrained Pre-pressure Beam Structure Attached with Multiple Nonlinear Energy Sinks

Dynamic Behavior and Vibration Suppression of a Generally Restrained Pre-pressure Beam Structure Attached with Multiple Nonlinear Energy Sinks

A pre-assessment and pollution prevention tool for indoor volatile organic compound simulations during the interior design stage

Volatile organic compounds (VOCs) are common indoor pollutants that can deteriorate indoor air quality. The pre-assessment of VOC concentrations aimed at pollution prevention during the interior design stage is effective in terms of time and expense for VOC control, as required by engineering applications. In this study, we introduce the concept, framework, and mathematical models of a pre-assessment and pollution prevention tool for VOC simulations. This tool considers multiple emissions sources and sinks, the interior construction schedule for the building materials, internal and external airflows, and air cleaning. The emissions/adsorption patterns from building materials were numerically coupled with the combined effects of the thermal environment, ventilation, indoor VOC concentrations, and emissions/adsorption from other materials at each time step, thereby improving the accuracy of the tool in engineering applications. The proposed tool was applied in an office building with multiple rooms and building materials to illustrate the simulation procedures and validate its effectiveness at VOC preventive control. The main emissions sources were determined and the selection of building materials was modified based on the simulation results. Field measurements were performed and the measured data were compared with the simulation results after construction completion. Differences between the simulated and measured results were predominantly within 0.02 mg/m3. The normalized mean square error was 0.11 and the mean relative standard deviation was 16.9%. This method can be used for indoor VOC concentration estimations, source contribution analysis, interior design scheme optimization, and ventilation rate requirement estimation, among other applications.

Vibration Reduction of a Timoshenko Beam with Multiple Parallel Nonlinear Energy Sinks

A nonlinear energy sink is a promising device to reduce structural vibrations. In this work, the vibration reduction performances of multiple parallel nonlinear energy sinks attached to a short beam are investigated based on the Timoshenko beam theory. The dynamic equations of a vibration reduction system subjected to a harmonic excitation are established. The frequency responses are analyzed based on Galerkin discretization and the harmonic balance method, and the accuracy is verified by the Runge–Kutta method. An optimization method based on the genetic algorithm is proposed for the number, location, and cubic stiffness of the nonlinear energy sinks. The study reveals that, with the same total mass, multiple parallel nonlinear energy sinks can achieve a larger vibration reduction ratio than a single nonlinear energy sink. The parameter influences of the nonlinear energy sinks are revealed, and unstable responses with large cubic stiffness are presented. The optimal locations of the multiple parallel nonlinear energy sinks are related to low-order modal shapes. A larger reduction ratio on the resonant amplitude can be achieved compared to a uniform distribution of the nonlinear energy sinks. The optimal locations and cubic stiffness are related to the number of nonlinear energy sinks. In the studied case, the optimal number of nonlinear energy sinks was two.

Complexity of minimum uplink power scheduling with delay bound for Backbone-assisted PD-NOMA wireless networks

Although it has high spectrum utilization ratio, the technique of Power-Domain Non-orthogonal Multiplexing Access (PD-NOMA) has a fatal disadvantage of high power consumptions. Backbone-Assisted PD-NOMA provides a feasible way to lowering power consumptions, since scheduling flexibilities are further enhanced by sharing both decoding capabilities of wireline-connected multiple sinks and the geographical diversity of the sinks. In this paper, we study the problem of minimizing uplink power consumption sum for backbone-assisted PD-NOMA wireless networks, under the constraint of given uplink delay bound. We formulate the problem, and prove that it is NP-Complete by presenting an original reduction from the classic problem of partition into triangles, which is known to be NP-Complete. We further propose a low-complexity heuristic algorithm based on the greedy strategy that as many user equipments as possible are simultaneously scheduled in one slot, which is also the optimal for one sink scenario. The analysis based on experimental results shows that the power consumption sum achieved by the heuristic algorithm will exponentially decrease with linearly-relaxed delay bound, and besides, it is virtually very close to the optimum.