Utilization Constraints Research Articles

Assessment of Alternative Rural Energy Sources and Technologies in South-Eastern Oromia Region, Ethiopia

Majority of world population (80% of total world) is living in rural area but consumes only about 30% of global commercial energy. In rural developing nations the energy sources for cooking and lighting are traditional sources with more prevalence in rural areas. Specially in Ethiopia, literatures indicated that more than 94% of the energy sources are from traditional sources. Poverty, lack of availability of modern energy and lack of education are the main causes of this phenomenon. Abundance use of biomass fuel with inefficient stoves caused scarcity of biomass resources in Ethiopia. However, in the study areas, namely Arsi and west Arsi, biomass energy sources and other energy sources and technologies under utilization were not characterized for research and development intervention. Therefore, this research proposal was initiated with objectives of characterizing energy resources and technologies under utilization and related constraints in the study areas. Four districts from Arsi and three districts from w/Arsi zones were selected and data was collected through FGD, KII and household level survey. The result revealed that majority of rural households are using biomass fuel (wood, animal dung and crop residues) specially for cooking purposes and there is expansion of solar cells for lighting purposes. The biogas plant use was under demonstration stages and it was also constrained with high capital investment and its accessory technology (injera stove).development of energy plants like jathropha is also underway in lowlands of Arsi zone. Keywords: “rural energy, biomass fuel, pure energy, improved cook stoves, Arsi zone” DOI : 10.7176/JETP/9-2-01

Assessment of Ficus thonningii tree production and utilization for livestock feed by smallholder farmers in northwestern Ethiopia

BackgroundAn assessment on the management practices and uses of Ficus thonningii tree by smallholder farmers’ was carried from December 2014 to March 2015 to evaluate the status of tree management, utilization as livestock feed and identify major production and utilization constraints. Three districts were purposely selected based on abundance of the tree from northwestern Ethiopia, and from each district, three peasant associations (smallest administrative unit) were selected purposely based on the abundance of F. thonningii availability and farmers’ utilization practices. The methodology of the study encompasses questionnaire, group discussion, key informant interview and secondary data sources. Descriptive statistics and linear regression were employed to present the quantitative and qualitative variables obtained from the survey.ResultsThe result indicated in all districts, livestock feed shortage during dry season was a critical problem. The ownership of F. thonningii tree indicated nonsignificant (P > 0.05) difference among the study districts. The types of livestock species reported feeding F. thonningii were cattle (54.2%), sheep (24.1%) and goats (21.7%) in all study districts. Among the parts of the tree used as feed, leaves in fresh form were used as dry season supplement to the livestock. The majority of respondents in the study districts reported to have accustomed practicing feeding F. thonningii leaves in the fresh from, followed by wilting and both fresh and wilting. Of the constraints of farmers in the study districts in using F. thonningii as feed for livestock were lack of training (51%), followed by land shortage (31%) and labor scarcity (18%).ConclusionsThis preliminary assessment indicated that the tree has valuable benefit to mitigate critical livestock feed scarcity in the dry season; farmers do have many constraints in its fullest utilization. Hence, to exploit the potential of F. thonningii as livestock fodder in northwestern Ethiopia, smallholder farmers should get training on production and utilization of the fodder tree.

Demonstrability of Narrowband Internet of Things technology in advanced metering infrastructure

The deployment cost of advanced metering infrastructure (AMI), one of the key elements for smart grid, often arises from three sub-systems, respectively referred to as data management system, communication network, and smart devices. Among them, the costs of communication network account for a considerable proportion. In smart grid, smart devices are generally fixed in a certain position and schedule reading of interval meter data. They do not only cover smart meters, but also extend gas, water meters, and other sensors measuring light, humidity, and temperature. Many of them are often with battery supply, and this will bring utilization constraints especially in communication network. The deployment needs of reducing cost and the energy-constrained network for an extremely large-scale AMI infrastructure both require optimum communication technologies that consider energy consumption, minimize energy use, simplify network topology, and prolong network lifetime. As an emerging 3GPP radio interface technology specifically designed for low power wide area networks (LPWANs), Narrowband Internet of Things (NB-IoT) inherits from the existing LTE but does many simplifications. Its most prominent features, enhanced coverage and low power consumption, are being pursued for a large-scale AMI communication network. In this paper, we provide a survey of AMI communication, emphasize on the key technologies of NB-IoT, analyze the performance of NB-IoT network over a real-world, and demonstrate the potential of NB-IoT to support and boost AMI in smart grid.

Online Location Trace Privacy: An Information Theoretic Approach

We consider the problem of protecting individual user’s location privacy at the trace-level and study the privacy-utility trade-off, which has key applications in privacy-preserving location-based service. Existing works on Location Privacy Protection Mechanisms (LPPMs) have mainly focused on protecting single location, without taking into account the temporal correlations among locations within the trace, which can lead to higher privacy leakage when considering the whole trace. However, to date, there lacks a formal framework to quantify the trace-level location privacy leakage, and a practical mechanism to release location traces in an optimal and online manner. In this paper, we endeavor to solve this problem using an information-theoretic approach. We first propose a location trace privacy metric based on the mutual information between the original and released trace in an offline setting, and formulate the optimal location trace release problem that minimizes trace-level privacy leakage given a utility constraint. We also propose a privacy metric to capture trace-level privacy leakage in an online setting. As directly computing these metrics incur exponential complexity w.r.t. the trace length, we obtain upper and lower bounds on the trace-level privacy leakage by exploiting the Markov structure of the temporal location correlations, which are efficiently computable. The proposed upper bounds enable us to derive efficient online solutions (i.e., LPPMs) by modifying Blahut-Arimoto algorithm in rate-distortion theory. Then we validate the proposed upper and lower bounds and the actual leakage of our LPPM through extensive experiments over both synthetic and real-world location data sets. Our results show the superiority of our LPPM over existing LPPMs in terms of trace-level privacy-utility tradeoff, which is more conspicuous when the location trace is more correlated.

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

Cloud computing enables users to purchase virtual resources on demand; therefore, the service requests change over time. Dynamic resource provisioning for cloud computing has become a key challenge. To reduce the associated costs, resource utilization must be improved, and ensure the Quality of Service (QoS) to meet the service-level agreements (SLAs). However, it is difficult to improve the resource utilization level and maintain a high QoS with fluctuating workloads in shared cloud computing systems. In this paper, we propose an adaptive control method for resource provisioning in cloud computing systems to simultaneously improve the resource utilization, achieve a satisfactory QoS, and react to the dynamic workload. We proposed an approach integrating adaptive multi-input and multi-output (MIMO) control and radial basis function (RBF) neural network to react to the highly dynamic workloads, and precisely control the resource allocation to improve resource utilization based on the maximum allowable QoS requirements. Experiments based on real-world workloads show that the proposed approach jointly improves resource utilization, maintains a satisfactory QoS, and handles workload fluctuations in a coordinated manner.

Multi-objective Optimization for Multi-task Allocation in Mobile Crowd Sensing

Mobile crowd sensing has attracted the attention of many researchers with the development of mobile phones. Many studies focus on the optimization of single-task oriented allocation, and most of them optimize only one objective. We consider multi-objective optimization for multi-task allocation in mobile crowd sensing with a limited participants pool. We formulate the problem for temporal-spatial coverage tasks. Two kinds of methods called constrain method and heuristic algorithm method are used to solve the multi-objective optimization problem. For the constrain method, we propose two new greedy algorithms: GMaxEOQU and GMinEOIP. GMaxEOQU pursues the max overall quality utility with budget constraint while GMinEOIP minimize the overall incentive payment with overall quality utility constraint. For the heuristic algorithm method, we propose a Pareto-optimal particle swarm optimization algorithm (PPSO) to search a set of Pareto-optimal solutions for the platform. Extensive experiments validate the performance of our algorithms.

Efficient and Fair Network Selection for Integrated Cellular and Drone-Cell Networks

This paper is concerned with the network selection for heterogeneous networks. Although many network selection approaches have been developed for heterogeneous networks, few of them explore the network selection problem for integrated cellular and drone-cell networks. Furthermore, most of the existing designs may be unsuitable for the integrated cellular and drone-cell networks owing to the high dynamic drone-cell-user association and the relatively limited drone-cell network capacity. In this paper, we investigate a network selection problem for the integrated cellular and drone-cell networks with a goal of maximizing a proportional fairness function of time average utilities across users under coarse correlated equilibrium constraints and minimum time average utility constraints. To mitigate this challenging problem, we first convert it into a non-linear integer programming (NLIP) problem based on our derived theoretical results. Next, we propose a repeated-stochastic-game-based efficient and fair network selection (RSG-EF) algorithm to alleviate the NLIP problem by leveraging a linear approximation mechanism. Simulation results show that the RSG-EF algorithm can achieve the highest total utility and a high level of fairness across users compared with three benchmark algorithms.

Computer Aided Analysis of Rotating Equipment Availability in Petrochemical Carbon Black Plant

High availability of industrial plant could only be achieved by adopting best global maintenance practices (computer software). In this research work, a software module was developed using programming tools such as Visual Basic (V.B) 6.0, Structured Query Language (SQL) and Microsoft Access. Operational data were collected from Warri Refinery and Petrochemical Company and was input into the developed software to analyze the plant availability. The performance test results obtained by the software for a five year period (2009-2013) showed that the software developed is consistently close to 100% accuracy. The output result indicates that the plant availability in 2009 is 9.59%; 2010 is 5.75%; 2011 is 7.12%; 2012 is 1.64% and 2013 is 6.30%. This highly fluctuating and low availability of raw material, inadequate maintenance strategies, aging plant and equipment, lack of spare parts stock for preventive and corrective maintenance for emergency, lack of standby (backup) equipment and utility constraints. It was observed that the aforementioned reasons invariably resulted to constant failure of plant availability could greatly be improved by adopting best global maintenance practices which include the deployment of appropriate software tools.

Computer Aided Analysis of Rotating Equipment Availability in Petrochemical Carbon Black Plant

High availability of industrial plant could only be achieved by adopting best global maintenance practices (computer software). In this research work, a software module was developed using programming tools such as Visual Basic (V.B) 6.0, Structured Query Language (SQL) and Microsoft Access. Operational data were collected from Warri Refinery and Petrochemical Company and was input into the developed software to analyze the plant availability. The performance test results obtained by the software for a five year period (2009-2013) showed that the software developed is consistently close to 100% accuracy. The output result indicates that the plant availability in 2009 is 9.59%; 2010 is 5.75%; 2011 is 7.12%; 2012 is 1.64% and 2013 is 6.30%. This highly fluctuating and low availability of raw material, inadequate maintenance strategies, aging plant and equipment, lack of spare parts stock for preventive and corrective maintenance for emergency, lack of standby (backup) equipment and utility constraints. It was observed that the aforementioned reasons invariably resulted to constant failure of plant availability could greatly be improved by adopting best global maintenance practices which include the deployment of appropriate software tools.

Integrated Gas – Electricity Network - Combined Heat and Power Optimization with Associated Petroleum and Wet Gas Utilization Constraint

Aside from liquid hydrocarbon, oil exploration also produces associated petroleum gas and wet gas. The use of associated petroleum and wet gas adversely affects gas turbine performance and gas line operation in several ways such as low gas quality, unstable heating value, and high H2S content. This research develops an integrated gas - combined heat and power optimal power flow with associated petroleum and wet gas utilization constraint. Thermodynamic equations are used to model gas turbine and gas network operation when subjected to low-quality fuels. To meet the fuel quality standard, additional constraints are considered. These constraints include the Modified Wobbe Index, a critical parameter for gas turbines, and H2S content, a critical parameter for pipelines. The results show that the proposed model can optimize combined heat and power cost by determining the most efficient power - steam dispatch, optimal fuel mixture and gas line pressure settings, while still meeting operational constraints.

Routing Perturbation for Traffic Matrix Evaluation in a Segment Routing Network

Traffic matrix (TM) assessment is a key issue for optimizing network management costs and quality of service. This paper presents a method to measure the intensity of ingress–egress traffic flows on an Internet service providers network that overcomes the limits of the classical measurement-based approaches. The proposed algorithm, called segment routing perturbation traffic (SERPENT), uses a routing perturbation approach enabled by the segment routing paradigm: The paths of a subset of flows are changed so that their intensities can be determined measuring the variation of the load of the network links. The TM is measured in successive steps, called snapshots, in which sets of flows are progressively re-routed and measured, under a maximum link utilization constraint. We state an integer linear programming (ILP) optimization problem to determine the flows to be rerouted in one snapshot. SERPENT is an heuristic offering an efficient solution to the stated ILP. Results show that SERPENT assesses the intensity of more than 80% of flows even when the network is highly stressed, while reducing the configuration cost with respect to classical approaches. Moreover, when used in conjunction with an estimation algorithm, SERPENT allows a reduction of the estimation error by more than 50% with fewer than 5 snapshots.

From verification to synthesis under cost-utility constraints

Various algorithms and tools for the formal verification of systems with respect to their quantitative behavior have been developed in the past decades. Many of these techniques inherently support the automated synthesis of strategies that guarantee the satisfaction of performance or reliability constraints by resolving controllable nondeterministic choices in an adequate way. More recently, such techniques have been further developed towards the analysis or strategy synthesis under multiple cost and utility constraints. This article deals with Markov decision processes (MDPs) for modeling systems and their environment and provides an overview of recent directions for different types of synthesis problems in MDPs that aim to achieve an acceptable cost-utility tradeoff.

Mining of skyline patterns by considering both frequent and utility constraints

Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability.

The optimization ordering model for intuitionistic fuzzy preference relations with utility functions

Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval-value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker’s judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision-maker’s utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs.

Constraints in Sugar Production and Utilization of New Improved Varieties in Enhancing Sugar Productivity in Yunnan Province, P. R. China

Sugarcane is a main cash crop in Yunnan Province, and nearly 10% populations of this area are involved in sugarcane cultivation and the industry. However, sugarcane and sugar industry are being confronted with serious challenges in China due to various reasons. Competition and comparing benefits with other crops, high cost, new and high yielding varieties, better technologies and management practices are the major thrust of the sugarcane industry in China. The current government takes action to reorganize this sector for its long-term economic sustainability. Yunnan Province has a sizeable contribution to sugar production in China, and therefore, holistic efforts are underway to improve the sugar productivity per se in the province. A few newly developed potential varieties have been evaluated to meet the need of the sugarcane industry. In this paper, production status and the major constraints along with the potential of bred sugarcane varieties in Yunnan Province are discussed.

Batch‐based security‐aware spectrum sharing with simultaneous assignment decisions in time‐critical IoT networks with cognitive radio capabilities

AbstractCognitive radio (CR) is considered as a key enabling communication technology that offers efficient wireless connectivity to Internet of Things (IoT) devices. Its integration with the future 5G architecture is expected to advance the IoT paradigm. Security attacks can severely degrade the performance of such CR‐based IoT (CRIoT) networks. The effect of security attacks can be reduced by implementing defensive approaches. However, such solutions come at the expense of degrading spectrum efficiency and consuming more network resources. In this paper, we investigate the spectrum sharing and access problem in a multidevice single‐transceiver CRIoT network with time‐critical applications under jamming attacks. Our main goal is to maximize the number of simultaneously served IoT devices over all available idle channels while ensuring delay requirement, hardware, link quality, security attacks, and spectrum utilization constraints. This problem is formulated as a total unimodular binary linear programming, which is shown to be solvable in polynomial time. Unlike most of previous security‐aware channel assignment solutions that conduct the channel assignment sequentially, our solution simultaneously provides secured distributed channel‐assignment decisions for multiple CRIoT links (batching method). Batching can be realized through an admission control stage for CR IoT devices to announce their control packets. Simulation results reveal that our solution significantly improves network performance compared to previous security‐aware schemes.

STUDY OF LOAD BALANCING TO MINIMIZE THE VARIABLE NODE POWER FOR INCREASING THE NETWORK LIFETIME IN WSN

Wireless technology growing rapidly now a days. Information congregation is a rapid increasing and challenging field in today's world of computing. This interacts with the various kinds of Things, Objects, Technological devices, Sensors in various fields. Wireless sensor networks consist of small battery powered devices with restricted energy resources. The main merit of the sensor network is that it includes power utilization constraint for sensor nodes with the help of battery life. The sensor nodes are typically out of the way for the user; therefore substitution of the energy source or battery is not practicable. For this reason, energy efficiency is a main design concern that wants to be improved in order to extend the lifetime of the sensor network. To extend the lifetime of sensor nodes, energy utilization and routing design issues are considered. The clustering based approach for communication is conceived as the mainly appropriate for the wireless sensor networks. The most important monitoring system is wireless sensor networks. All of the sensor nodes act as an event detector as well as a data router. It consists in selecting a set of cluster heads from the set of sensor nodes. The cluster members transmit the data to the cluster head and all received data at cluster head is aggregated and transmit it to the base station. Clustering reduces and balances the energy utilization and prolongs the lifetime and scalability of the wireless sensor network. Clustering is frequently used with a data aggregation method. Proposed method is work in three phases; Initialization Phase, Setup Phase, Transmission Phase.n

Optimal quota-share reinsurance based on the mutual benefit of insurer and reinsurer

This paper investigates the optimal quota-share reinsurance strategies that can bring mutual benefit to both an insurer and a reinsurer. We consider five different optimality criteria that reflect the interest of both parties. The mutual beneficiary is also reflected in utility improvement constraints, which guarantee that both the insurer and the reinsurer will end up with higher expected utility of wealth with reinsurance agreements. Under each optimality criterion, explicit expressions of optimal quota-share retention and the corresponding objective function are obtained. Results indicate that the reinsurer’s safety loading plays a key role in determining the optimal retained proportion. In the numerical example, we demonstrate the expected utility increment after underwriting optimal constrained quota-share reinsurance that minimizes the total VaR/TVaR of the two parties. Comparing with the optimal strategies without utility constraint, the total expected utility will increase significantly after entering into an optimal quota-share reinsurance contact with utility improvement constraints.

Assessment of Indigenous Knowledge and Its Relevance for Livestock Production: the case of North Gondar zone: Amhara Region, Ethiopia

The study was conducted in North Gondar Zone, Amhara region Ethiopia, to explore the various indigenous knowledge systems practiced in livestock production by small holder farmers. Both qualitative and quantitative data were collected from primary and secondary data sources. Qualitative data that helped to assess indigenous knowledge practices in the study area were collected through personal observation, and focus group discussions. In addition to this semi-structured questionnaires were distributed to 120 Households spread across the three districts of different PA administrative to collect relevant data. The data collected through questioner was organized and analyzed by using SPSS (version 20). The study revealed that a combination of crop residue and natural pasture is common sources of feed for most (45.8 %) of respondents. The indigenous knowledge regarding livestock production is considered as old as domestication of various livestock species. With regard to feed treatment practices, most (51.7%) of the farmers in the area treat the animal feed by using common salt in order to increase food palatability by the animals. Ethno-veterinary medicines is one of the common practice by households in North Gondar Zone. Even though most (65%) of households prefer modern disease treatment, about 78.3 % of farmers are using cultural medicine for treating different livestock diseases The main reason for using cultural medicine by farmers in the area is it’s easily availability, cheapest cost and its practical applicability. The major constraint of indigenous knowledge utilization in north Gondar zone is the resistance Development agents (28.3%) followed by other administrative bodies (22.5%) in the region. There is a high risk of extinction of indigenous knowledge if it is not documented. To avoid this loss, emphasis should be given on developing indigenous knowledge systems and documenting this unique knowledge into the mainstream knowledge. In addition to this, research should be done on the various methods of ethno-veterinary medicine and document the effective ones for use by the future generations. Moreover, research from a gender perspective is needed to document specialized ethno-veterinary knowledge not only of cattle but also of small stock such as sheep and chickens. It is important that livestock farmers share ideas on traditional knowledge with modern veterinarians in order to optimize productive capacity of herds and enhance sustainable rural livelihoods.

Combined heat and power – multi-objective optimization with an associated petroleum and wet gas utilization constraint

Associated petroleum gas and wet gas are byproducts of oil field operations. The use of both gases as low-cost, low-quality fuel in oil field operations has several downfalls, such as a low Modified Wobbe Index and high hydrogen sulfide content. Modified Wobbe Index represents fuel interchangeability which affects gas turbine reliability. High hydrogen sulfide content may result in failures on the gas pipeline. The purpose of this research is to develop a multi-objective combined heat and power optimization by considering associated petroleum and wet gas utilization. The optimization model utilizes thermodynamic equations to represent gas turbine and heat recovery steam generator performance when subjected to low-quality fuel. Optimization also considers power system parameter as constraints. Three conflicting objective functions will be considered: cost vs. Modified Wobbe Index vs. hydrogen sulfide content. Optimization will use two algorithms for comparison purposes: Goal Attainment and NSGA-II. The result shows that the optimization model can analyze the tradeoffs between the three conflicting parameters.