Upgrade Costs Research Articles

Analysis of machine learning based LEACH robust routing in the Edge Computing systems

Wireless sensor networks (WSN) are used to detect real-time changes in the deployed environment. This dynamic behaviour is either triggered by the deployed environment or by the user from outside. Because of their ability to monitor complex scenarios that change rapidly over time, wireless sensor networks are critical components of most advanced computing systems. These complex activities are influenced by different methods or even by the designers of their networks. Machine learning encourages many real solutions that optimise resource use and increase the network's lifespan in sensor networks. LEACH routing protocol has many limitations due to sudden energy utilisation & cluster head nodes due to direct communication with the base station node. This fast node energy leak creates several black hole structures in the networks, resulting in data redundancy, data packets transmission, node upgrade costs, and end-to-end delay for WSN. The proposed model with LEACH protocol functionality has improved network performance, network (WSN) efficiency, and solving data redundancy issues. By using an independent Recurrent Neural Network (IRNN)-based data fusion algorithm, namely, DFAIRNN. The simulation and comparative results indicate that the mean method & minimum distance method used in the LEACH-DFAIRNN protocol can effectively resolve data redundancy issues caused by the adjacent sensor nodes by flooding data simultaneously to a single node.

An Optimal IoT Device Placement Strategy for Agro-IoT Using Edge Computing

Background: Development in innovation for remote detecting and mechanization have persuaded the wide selection of the mechanical improvements in all fields of studies and applications. Objective: Fundamentally, the countries with high reliance on horticulture have improved the conventional support of the harvests and procedures with the summon of advances like remote detecting or sensor-based systems or Internet of Things. Methods: The consolidation of trend setting innovation has exhibited high effect on profitability and reachability. The misfortunes because of the catastrophic events have essentially decreased as a similar time. The adjustments of these innovations were conceded because of the underlying information gain. The real bottlenecks of the adjustment of IoT for horticulture were three. Initially, the advancement of the money saving advantages can't be accomplished because of the impediment of the innovation. Furthermore, the inconsistent conveyance of the figuring forces makes the total IoT organize inflexible for making further upgrades or expensive to keep up. At long last, the structure for district explicit agro-IoT is as yet the interest for the business. Results: Consequently, this work proposes a novel IoT gadget situation technique with the advantages of cost enhancements and upgrades over the transmissions. Likewise, the advantages from the edge figuring instrument are accomplished in this work. Conclusion: At long last, this work likewise contributes towards the locale explicit system for Agro-IoT with the intension of making the monetarily provoked countries to likewise profit the advantages of innovation shifts.

The sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees.

Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed cases can be described as a rooted tree that has a weight of infectious intensity for each edge. Upgrading edges (nodes) can reduce the infectious intensity with contacts by taking prevention measures such as disinfection (treating the confirmed cases, isolating their close contacts or vaccinating the uninfected people). We take the sum of root-leaf distance on a rooted tree as the whole infectious intensity of the tree. Hence, we consider the sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees (SDIPT-UE/N). The problem (SDIPT-UE) aims to minimize the sum of root-leaf distance by reducing the weights of some critical edges such that the upgrade cost under some measurement is upper-bounded by a given value. Different from the problem (SDIPT-UE), the problem (SDIPT-UN) aims to upgrade a set of critical nodes to reduce the weights of the edges adjacent to the nodes. The relevant minimum cost problem (MCSDIPT-UE/N) aims to minimize the upgrade cost on the premise that the sum of root-leaf distance is upper-bounded by a given value. We develop different norms to measure the upgrade cost. Under weighted Hamming distance, we show the problems (SDIPT-UE/N) and (MCSDIPT-UE/N) are NP-hard by showing the equivalence of the two problems and the 0–1 knapsack problem. Under weighted l_1 norm, we solve the problems (SDIPT-UE) and (MCSDIPT-UE) in O(n) time by transforimg them into continuous knapsack problems. We propose two linear time greedy algorithms to solve the problem (SDIPT-UE) under unit Hamming distance and the problem (SDIPT-UN) with unit cost, respectively. Furthermore, for the the minimum cost problem (MCSDIPT-UE) under unit Hamming distance and the problem (MCSDIPT-UN) with unit cost, we provide two O(nlog n) time algorithms by the binary search methods. Finally, we perform some numerical experiments to compare the results obtained by these algorithms.

Green Multi-Stage Upgrade for Bundled-Link SDNs With Budget and Delay Constraints

Upgrading a legacy network to a Software Defined Network in stages, and minimizing the energy consumption of a network are now of great interest to operators. To this end, this paper addresses a novel problem: minimize the energy consumption of a network by upgrading switches over multiple stages subject to the available monetary budget at each stage. Our problem considers (i) bundled links that can be powered-off individually, (ii) decreasing upgrade cost and increasing traffic demands over multiple stages, and (iii) rerouting demands to an alternative path with a delay that is within a predefined limit. Additionally, the link load on the path is no larger than a given maximum utilization. We formulate the problem as an Integer Linear Program and propose a greedy heuristic called Green Multi-Stage Switch Upgrade (GMSU). Experiment results on five actual network topologies show that: 1) our approaches reduce the energy consumption by up to 73.98%; 2) rerouting traffic demand via longer paths improves the energy saving by up to 9.6%; 3) GMSU produces results that are at most 4.83% from the optimal result; and 4) increasing the budget and the number of stages affect the total energy saved and the number of upgraded switches.

Service-enhancement Investments and Price Competition in a Hotelling Model

This paper conducts a study on the competition of service and price under the circumstances of duopoly in the Hotelling linear market, of which the results indicate that the unit variable cost of service level upgrade is large, the service investment undertaken by each firm is insufficient. Besides, we have also taken the situation of government price regulation into consideration, showing that proper price regulation is available to stimulate investment, and the government can apply regulated prices to effectively controlling the level of investment in services.

Optimising the design and real-time operation of systems of distributed stormwater storages to reduce urban flooding at the catchment scale

The impact of urban flooding is increasing due to the effects of increasing urbanisation and climate change. The use of storages is a relatively well-established approach to reduce peak flows and therefore reduce the need for costly upgrades to stormwater conveyance infrastructure. Recently, real-time control (RTC) has been considered as a means of increasing the performance of these storages. However, previous RTC studies have generally focussed on evaluating RTC with pre-determined storage layouts, i.e., storage locations and volumes, and have ignored the impact of optimising the storage layout. Hence, the objectives of this paper are (i) to introduce a two-step approach to minimising peak flows at the catchment scale by firstly optimising the layout of distributed storages and then optimising their RTC strategies, and (ii) to test the effectiveness of this approach on a real catchment in Adelaide, South Australia. Results show that distributed storage with optimised layouts can achieve significantly higher peak flow reductions than more commonly used end-of-system storage. These range from 5% for 100 m3 of system storage up to 40% for 700 m3 of system storage – whereas end-of-system storage achieved no peak flow reduction for up to 700 m3 of storage. The addition of optimised RTC to distributed storages is able to achieve an additional 10% in peak flow reduction. This increase in performance due to RTC is more significant for smaller system storage volumes. Analysis of the flood hydrographs shows the system peak flow reductions are achieved by attenuating hydrographs at individual storages, as well as delaying hydrographs to reduce their coincidence at the confluence of sub-catchments. These results highlight the potential for using optimised distributed storage layout and RTC as an alternative approach to end-of-system storage to reduce flood peaks.

Analysis and selection of optimal solvent-based technologies for biogas upgrading

Biogas upgrading is an important industrial process for producing biomethane, a sustainable energy source with low carbon footprint. There are three main solvent-based alternatives for biogas upgrading: water scrubbing, physical scrubbing and chemical scrubbing with amines. Though assessments have been published regarding which technologies are more cost-effective and energetically efficient, these often either neglect inspecting the impact of raw biogas concentrations and biomethane delivery pressures on the overall performance of the plant, or they do not consider that the separated CO2 has to be conditioned for transportation for properly fulfilling the requirements of BECCS (bioenergy with carbon capture and storage). In fact, many assessments of physical scrubbing processes forfeit CO2 recovery altogether. This work intends to rehabilitate physical scrubbing as an alternative for BECCS by showing that, depending on the conditions of the raw biogas, the requirements for biomethane delivery, and the cost of carbon taxes, biogas upgrading can be feasible by scrubbing either with amines or with organic solvents. We introduce a review on organic physical solvents for CO2 recovery, a simulation framework for the evaluation of energetical operational costs of biogas upgrading, and a simplified economic analysis. High biomethane delivery pressures and high CO2 concentrations in raw biogas benefit the use of physical solvents such as N-formyl-morpholine, N-methyl-2-pyrrolidone and poly(ethylene glycol)dimethyl ether, whereas the opposite conditions are advantageous to aqueous monoethanolamine. Finally, the implementation of carbon taxes of around 10 USD/ton CO2 emitted are sufficient to increase the attractiveness of CO2 recovery as opposed to CO2 wasting.

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Optimizing locations of waste transfer stations in rural areas.

Many studies have considered the location of rural waste transfer stations, but most have omitted the impact of transportation network conditions. Traffic accessibility must be considered in optimizing the location of rural waste transfer stations, which is an important difference from the location of rural waste transfer stations. On the basis of previous studies, this study will consider the impact of traffic network on the optimization locations of waste transfer station in the rural areas. The objective of this study was to ensure the minimum Euclidean distance between the waste transfer station and the population center is the maximum, minimize the garbage transportation cost of each population center, construction costs for waste transfer stations, construction and upgrade costs for roads on a traffic network. A multi-objective facility location-network design model and an improved multi-objective simulated annealing algorithm was used to solve the problem. A detailed practical case study was used to illustrate the application of the proposed mathematical model. The results show that transportation network plays an important role in facility location optimization, and the improvement of traffic network conditions can greatly reduce waste transportation costs.

A Control Strategy to Mitigate the Sensitivity Deterioration of Overcurrent Protection in Distribution Networks With the Higher Concentration of the Synchronous and Inverter-Based DG Units

This article focuses on the indeterminate protection challenge of the sensitivity deterioration (SD) (also known as protection blindness) of the overcurrent protection (OCP) devices due to the large-scale integration of distributed energy resources (DERs) in the unbalanced distribution networks (DNs). The protection blindness of the OCP has been demonstrated through novel mathematical formulations to quantify the percentage of the SD for different types of distributed generation (DG) units and for the varying DG penetration levels. Furthermore, this issue has been realistically analyzed for the different fault impedances and DG locations to examine its impact on the existing OCP system. A mitigation strategy has been proposed to overcome the SD issue of the OCP protective devices (PDs) for a steadfast operation of the OCP devices without requiring costly upgrades of the protection system. This protection blindness issue has been explored and the mitigation strategies have been tested for both synchronous and inverter-based DGs using the IEEE standardized test feeders. The proposed mitigation strategy has been validated in the IEEE 34-bus test system. The simulation results prove the effectiveness of the proposed mitigation strategy to restore the protection coordination and suitability to improve grid resilience.

Paid Peering, Settlement-Free Peering, or Both?

With the rapid growth of congestion-sensitive and data-intensive applications, traditional settlement-free peering agreements with best-effort delivery often do not meet the QoS requirements of content providers (CPs). Meanwhile, Internet access providers (IAPs) feel that revenues from end-users are not sufficient to recoup the upgrade costs of network infrastructures. Consequently, some IAPs have begun to offer CPs a new type of peering agreement, called paid peering, under which they provide CPs with better data delivery quality for a fee. In this article, we model a network platform where an IAP makes decisions on the peering types offered to CPs and the prices charged to CPs and end-users. We study the optimal peering schemes for the IAP, i.e., to offer CPs both the paid and settlement-free peering to choose from or only one of them, as the objective is profit or welfare maximization. Our results show that 1) the IAP should always offer the paid and settlement-free peering under the profit-optimal and welfare-optimal schemes, respectively, 2) whether to simultaneously offer the other peering type is largely driven by the type of data traffic, e.g., text or video, and 3) regulators might want to encourage the IAP to allocate more network capacity to the settlement-free peering for increasing user welfare.

Evaluating the Curtailment Risk of Non-Firm Utility-Scale Solar Photovoltaic Plants under a Novel Last-In First-Out Principle of Access Interconnection Agreement

With the increasing share of distributed energy resources on the electric grid, utility companies are facing significant decisions about infrastructure upgrades. An alternative to extensive and capital-intensive upgrades is to offer non-firm interconnection opportunities to distributed generators, via a coordinated operation of utility scale resources. This paper introduces a novel flexible interconnection option based on the last-in, first-out principles of access aimed at minimizing the unnecessary non-firm generation energy curtailment by balancing access rights and contribution to thermal overloads. Although we focus on solar photovoltaic (PV) plants in this work, the introduced flexible interconnection option applies to any distributed generation technology. The curtailment risk of individual non-firm PV units is evaluated across a range of PV penetration levels in a yearlong quasi-static time-series simulation on a real-world feeder. The results show the importance of the size of the curtailment zone in the curtailment risk distribution among flexible generation units as well as that of the “access right” defined by the order in which PV units connect to the grid. Case study results reveal that, with a proper selection of curtailment radius, utilities can reduce the total curtailment of flexible PV resources by up to more than 45%. Findings show that non-firm PV generators can effectively avoid all thermal limit-related upgrade costs.

Architectural Dependency Analysis: Addressing the Elephant in the Room

Dependency analysis is typically limited to the static analysis of code structures. We applied this practice to safety-critical systems that were re-engineered to reduce safety testing and technology upgrade cost. We discuss the need for a well-defined description of architectural dependencies to address the observed gaps.

Optimizing the integration of renewable energy in existing buildings

This paper presents the development of a novel optimization model for optimizing the use of RE measures in existing buildings to satisfy an owner-specified target reduction in energy consumption while minimizing the required building upgrade costs. The model performance was evaluated using a real-life case study of an educational building with a total area of 20,717 m2. The optimization model was used to identify an optimal combination of RE technologies that minimize the upgrade cost for this case study while satisfying its specified 80% energy reduction goal. The model minimized the building upgrade cost by making a number of optimal selections, such as locating the majority of solar RE technologies in building segments facing south due to their higher exposure to the sun radiations. The results of this analysis highlight the original contributions of the model in identifying an optimal set of RE measures that minimize the building upgrade cost while satisfying all owner-specified requirements, providing a detailed description of the optimal building integration plan of RE technologies, and complying with practical functional requirements, and constructability constraints. These capabilities are expected to support building owners and decision-makers in their efforts to integrate cost-effective RE measures in existing buildings.

The Efficiency of Window Design and Orientation on Long-Cycle Passive Solar Heating Demands in Residential Buildings.(Dept.A)

This paper describes research conducted to evaluate the efficiency of various window components with respect to solar heating demands of a prototype residential house in Waterloo, Canada. A number of proposed high performance window types are compared against base-case conventional windows in attempt to trace the long cycle cutback in heating demands. Three window orientations including South, North and West facing were considered. RetScreen passive solar heating project model was used for this purpose. The upgrade of windows has shown a dramatic cutback in passive solar heating demands with preference in South facing direction. Initial cost of window upgrade is reimbursed, due to reduction of active solar heating cost, in less than ten years depending on the type of window selected. it is concluded that the economical upgrade of a window depends on the initial installation cost required for the upgrade and the project life.

Maximum shortest path interdiction problem by upgrading edges on trees under hamming distance

We consider the maximum shortest path interdiction problem by upgrading edges on trees under Hamming distance (denoted by (MSPITH)), which has wide applications in transportation network, network war and terrorist network. The problem (MSPITH) aims to maximize the length of the shortest path from the root of a tree to all its leaves by upgrading edge weights such that the upgrade cost under sum-Hamming distance is upper-bounded by a given value. We show that the problem (MSPITH) under weighted sum-Hamming distance is NP-hard. We consider two cases of the problem (MSPITH) under unit sum-Hamming distance based on the number K of critical edges. We propose a greedy algorithm within $$O(n+l\log l)$$ time when $$K=1$$ and a dynamic programming algorithm within $$O(n(\log n+K^3))$$ time when $$K>1$$ , where n and l are the numbers of nodes and leaves in a tree, respectively. Furthermore, we consider a minimum cost shortest path interdiction problem by upgrading edges on trees under unit Hamming distance, denoted by (MCSPITUH) and propose a binary search algorithm within $$O(n^4\log n)$$ time, where a dynamic programming algorithm is executed in each iteration to solve its corresponding problem (MSPITH). Finally, we design numerical experiments to show the effectiveness of the algorithms.

Future cloud: spherical processors for realizing low-cost upgrade in underwater data centers

Underwater data centers are appealing due to their ability to benefit from the ocean cooling. However, upgrading the computing capability of underwater data centers is challenging because they are not easily accessible. Nevertheless, underwater data centers should be able to process increasing workloads. This paper addresses the challenge of upgrading underwater data centers by proposing the geometric processor paradigm (GPP). In GPP, underwater data centers use spherical processors with a higher surface area than conventional processors and host more processing units. GPP also incorporates the processor pause feature (PPF) that enables to consider the power available to an underwater data center. The research evaluates how the use of spherical processors enhances computational capability and reduces upgrade costs. The evaluation shows that the use of spherical processors enhances computational capability and reduces data centers interrupt epochs by up to 95% and 79% on average, respectively.

Multi-Path Routing in Green Multi-Stage Upgrade for Bundled-Links SDN/OSPF-ECMP Networks

This paper considers the novel problem of upgrading a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.

Going Beneath the Surface: Petroleum Pollution, Regulation, and Health

This paper quantifies the health impacts of petroleum leaks from underground storage tanks, the effectiveness of tank regulation, and the role of information as a policy tool in the same setting. Exposure to a leaking underground storage tank during gestation increases both the probability of low birthweight and preterm birth by 7–8 percent. Compliance with regulations requiring the adoption of preventative technologies mitigated the entire effect of leak exposure on low birthweight, and information increased avoidance and moving among highly educated mothers. Back-of-the-envelope calculations suggest the health benefits of preventative regulations exceed the upgrade cost to facilities. (JEL I12, K32, L71, L78, Q35, Q51, Q53)

EFRADIR: An Enhanced FRAmework for DIsaster Resilience

This paper focuses on how to increase the availability of a backbone network with minimal cost. In particular, the new framework focuses on resilience against natural disasters and is an evolution of the FRADIR/FRADIR-II framework. It targets three different directions, namely: network planning, failure modeling, and survivable routing. The steady state network planning is tackled by upgrading a sub-network (a set of links termed the spine) to achieve the targeted availability threshold. A new two-stage approach is proposed: a heuristic algorithm combined with a mixed-integer linear problem to optimize the availability upgrade cost. To tackle the disaster-resilient network planning problem, a new integer linear program is presented for the optimal link intensity tolerance upgrades together with an efficient heuristic scheme to reduce the running time. Failure modeling is improved by considering more realistic disasters. In particular, we focus on earthquakes using the historical data of the epicenters and the moment magnitudes. The joint failure probabilities of the multi-link failures are estimated, and the set of shared risk link groups is defined. The survivable routing aims to improve the network’s connectivity during these shared risk link group failures. Here, a generalized dedicated protection algorithm is used to protect against all the listed failures. Finally, the experimental results demonstrate the benefits of the refined eFRADIR framework in the event of disasters by guaranteeing low disconnection probabilities even during large-scale natural disasters.