Schedule Length Research Articles

Link Schedulers for Green Wireless Networks With Energy Sharing

This paper considers wireless link scheduling in a new setting: nodes with Energy Harvesting (EH) as well as energy sharing capabilities. We propose a Mixed Integer Linear Program (MILP) to determine 1) the shortest link schedule, whereby active links in the same slot adhere to the physical interference model; 2) the optimal transmission power of active links; and 3) the amount of energy shared by nodes in each slot. We also outline a novel heuristic algorithm to generate a link schedule for large-scale networks. In addition, we outline a distributed protocol that is used by nodes to independently determine whether to share energy and/or transmit data in each slot. Our results show that 1) energy sharing reduces schedule lengths by about 20% and 2) our distributed algorithm generates schedule lengths that are approximately 65% shorter as compared to a protocol that requires nodes to decide randomly whether to transmit data or/and share energy.

A novel hybrid heuristic-based list scheduling algorithm in heterogeneous cloud computing environment for makespan optimization

An efficient workflow scheduling can potentially exploit heterogeneity of resources in heterogeneous cloud computing (HCC) platform commensurate with variable requirement of dependent tasks in a given workflow. Minimizing the total scheduling length, makespan, is essential for application performance in heterogeneous computing systems especially in cloud computing environment. The problem of scheduling a set of different dependent tasks onto a set of heterogeneous computational resources is a well-known NP-Hard problem. Therefore, no polynomial scheduling algorithm for computing the optimal solution exists. For approximating a solution to this problem many algorithms have been proposed, but majority of them have low efficiency. In this paper, a novel hybrid heuristic-based list scheduling (HH-LiSch) algorithm is presented for solving the dependent task scheduling in HCC systems in a bounded number of the fully connected virtual machines (VMs). The novelty of the current paper is to present the new task priority strategy, find appropriate VM's slot time, and utilize task duplication technique. Two novel task priority strategies are applied to prioritize tasks in an efficient ordered list. Then, during the scheduling process an insertion-based procedure is called to find an appropriate potential slot time for performing task duplication technique. If it works, the task duplication is added to rudimentary scheduling scheme. In this way, the final scheduling is gradually generated. To validate the work, the experiments are based on six real-world scientific workflows and a random task graph (RTG); then, the performance is evaluated in terms of makespan, Schedule Length Ratio (SLR), speedup and efficiency. The simulation results prove a significant improvement against other counterparts in literature.

Scheduling Multiprocessor Tasks with Equal Processing Times as a Mixed Graph Coloring Problem

This article extends the scheduling problem with dedicated processors, unit-time tasks, and minimizing maximal lateness Lmax for integer due dates to the scheduling problem, where along with precedence constraints given on the set V={v1,v2, …,vn} of the multiprocessor tasks, a subset of tasks must be processed simultaneously. Contrary to a classical shop-scheduling problem, several processors must fulfill a multiprocessor task. Furthermore, two types of the precedence constraints may be given on the task set V. We prove that the extended scheduling problem with integer release times ri≥0 of the jobs V to minimize schedule length Cmax may be solved as an optimal mixed graph coloring problem that consists of the assignment of a minimal number of colors (positive integers) {1,2, …,t} to the vertices {v1,v2, …,vn}=V of the mixed graph G=(V,A, E) such that, if two vertices vp and vq are joined by the edge [vp,vq]∈E, their colors have to be different. Further, if two vertices vi and vj are joined by the arc (vi,vj)∈A, the color of vertex vi has to be no greater than the color of vertex vj. We prove two theorems, which imply that most analytical results proved so far for optimal colorings of the mixed graphs G=(V,A, E), have analogous results, which are valid for the extended scheduling problems to minimize the schedule length or maximal lateness, and vice versa.

Operating Room (Re)Scheduling with Bed Management via ASP

AbstractThe Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set Programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small–medium-sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, that is, when the offline schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time.

An Integrated Co-Design of Flow-Based Biochips Considering Flow-Control Design Issues and Objectives

With increasing effectiveness of flow-based microfluidic biochips in the field of biochemical experiments and point-of-care diagnosis, design automation demands enormous attention to integrate the flow-layer design and control-layer design instead of considering them in isolation. Some severe design issues like valve reliability, control-channel delay, pressure skew, etc. are obvious and they must be tactfully dealt along with minimization of several cost-driving issues like schedule length, pin count, and control-channel length while ensuring planarity of the flow-layer components as well as control-layer channel routes. Hence, this paper aims to propose a unified flow-control co-design methodology considering various flow-control cost-driving issues while reducing the design cycles to obtain a low-cost and efficient platform. The performance is evaluated over several benchmark bioassays that show the efficacy of the proposed co-design method which reduces the number of required valves by 30%, control-channel length by 48.5%, and number of flow-channel crossings by 72.2% than the existing works, on an average.

A Self-Organized Scheduling Algorithm for Embedded Real-Time Systems

In this paper, the authors present a self-organized approach for scheduling tasks on processors in embedded real-time systems. For such a mapping, two conflicting objectives have to be optimized: the reliability and the schedule length. Since the scheduling problem is NP-hard, a heuristic algorithm is used to produce schedules achieving different trade-offs between the two objectives. Moreover, a self-organization strategy based on dynamic crowding distance is adopted. This allows a better exploration of the objective space as well as an enhanced solution diversity. The proposed algorithm, reliability schedule length trad-offs algorithm (RSTA), is tested and compared with the popular SPEA2 algorithm where experimental results are promising on both quality and diversity of solutions.

An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

An improved list-based task scheduling algorithm for fog computing environment

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

Pegfilgrastim-induced leukocytosis and hyperleukocytosis

Objective: To report a case of leukocytosis (LCT) and hyperleukocytosis (HLCT) episodes post Pegfilgrastim (Neulasta) administration.Case Summary: A 74-year-old female presented with several episodes of LCT and HLCT following administration of Pegfilgrastim while undergoing adjunct dose dependent chemotherapy for adenocarcinoma of the gallbladder. The patient had completed cycle 6, day 8 of chemotherapy and subsequently received Neulasta 48 hours later. Two days later, she presented to the ER with white blood cell (WBC) count of 110K. Prior to Neulasta administration, her WBC counts were within normal range and after each episode of leukocytosis, the patient’s WBC count trended downward. Upon consultation, hematology considered Pegfilgrastim as a likely cause for this patient’s WBC cycling and HLCT.Discussion: Pegfilgrastim-induced HLCT occurs in less than 1% of patient cases. Dose-dependent chemotherapy combined with Pegfilgrastim treatment is an optimal treatment option to reduce the length of chemotherapy schedules and risk of febrile neutropenia. Following the dosing of Pegfilgrastim, the drug clearance is mediated by neutrophil receptors which results in a reduction of ANC values.Conclusions: Further studies are needed to determine the optimum timing and dosage of Pegfilgrastim to offer maximum myeloprotective benefit while also minimizing the risks of adverse events such as leukocytosis and hyperleukocytosis experienced by our patient.

Bio-Inspired Workflow Scheduling on HPC Platforms

Efficient scheduling of tasks in workflows of cloud or grid applications is a key to achieving better utilization of resources as well as timely completion of the user jobs. Many scientific applications comprise several tasks that are dependent in nature and are specified by workflow graphs. The aim of the cloud meta-scheduler is to schedule the user application tasks (and the applications) so as to optimize the resource utilization and to execute the user applications in minimum amount of time. During the past decade, there have been several attempts to use bio-inspired scheduling algorithms to obtain an optimal or near optimal schedule in order to minimize the overall schedule length and to optimize the use of resources. However, as the number of tasks increases, the solution space comprising different tasks-resource mapping sequences increases exponentially. Hence, there is a need to devise mechanisms to improvise the search strategies of the bio-inspired scheduling algorithms for better scheduling solutions in lesser number of iterations/time. The objective of the research work in this paper is to use bio-inspired bacteria foraging optimization algorithm (BFOA) along with other heuristics algorithms for better search of the scheduling solution space for multiple workflows. The idea is to first find a schedule by the heuristic algorithms such as MaxMin, MinMin, and Myopic, and use these as initial solutions (along with other randomly generated solutions) in the search space to get better solutions using BFOA. The performance of our approach with the existing approaches is compared for quality of the scheduling solutions. The results demonstrate that our hybrid approach (MinMin/Myopic with BFOA) outperforms other approaches.

A hybrid list-based task scheduling scheme for heterogeneous computing

Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This work presents a Hybrid List-based Task Scheduling using Duplication Scheme (HLTSD) algorithm for heterogeneous processors. The proposed HLTSD algorithm has the same time complexity as that of the recent state-of-the-art algorithms. However, it produces a minimum cost schedule in comparison with other related methods. This work also presents a mathematical formulation to find task priorities. The processor selection phase is improved by utilizing the techniques, like entry task duplication, insertion-based policy, duplication of parent task on other levels, and balancing the load on each processor. The current proposal minimizes the overall makespan of execution by reasonable levels. Performance of the proposed algorithm is evaluated using DAGs adopted from various state-of-the-art algorithms, real-world problems, like Gaussian elimination (GE) and fast Fourier transformation (FFT) task graph and randomly generated graphs with diverse characteristics. The proposed scheme is compared with four state-of-the-art list-based scheduling algorithms, namely Heterogeneous Earliest Finish Time (HEFT), Predict Earliest Finish Time (PEFT), Heterogeneous Scheduling with Improved task Priority (HSIP), and Task Scheduling for Heterogeneous Computing Systems (TSHCS). Based on the best quality schedule, the obtained results suggest that HLTSD has better results in 87% cases.

An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

Nurse coordinator roles in the management of patients with hepatocellular carcinoma: A French national survey

Hepatocellular carcinoma (HCC) mostly occurs in patients with chronic liver disease (CLD). HCC treatment may have a direct impact on CLD prognosis. HCC management can therefore become complex, involving multiple health care providers, such as oncologists, hepatologists, radiologists, and surgeons. In France, dedicated nurses have been involved in patient care pathways. Their impact is poorly documented. PurposeTo determine the country-wide distribution of HCC nurse coordinators in French health care settings and to describe their roles and responsibilities. Patients and methodsA survey using a multi-item questionnaire (including center characteristics, nurse coordinator characteristics, and quality indicators such as patient care pathway initiation timeline, scheduled length of hospital stay, diagnostic disclosure process) was conducted. All French liver cancer centers planning to participate in a prospective national cohort study for patients with HCC (CHIEF Cohort) were invited to take part in the survey. Bivariate analysis compared centers with a nurse coordinator to those without. ResultsAmong the 42 of 72 centers that replied, 14 treated fewer than 75 HCC patients. Treatment mostly took place in hepatology units (34/42). Sixteen nurse coordinators were part of the health care team in 13 of the 42 centers. Among these 13 centers, 11 were university hospitals and 11 followed more than 75 patients per year. The median number of patients followed in these centers was 300 (min–max 44–600) in 2017. All nurse coordinators were involved in providing patient information and counseling. Other roles included treatment monitoring (13/16), care coordination (12/16), psychological support (12/16) and treatment planning (11/16). Thirteen nurse coordinators conducted diagnostic disclosure nurse consultations; seven conducted initial patient contact consultations; and six held outpatient nurse consultations, with wide heterogeneity between centers. The presence of a nurse coordinator was associated with completion of the full diagnostic disclosure process (p = 0.045). ConclusionIn France, nurse coordinators for HCC patient pathway management are present mainly in university hepatology units with a caseload of more than 75 patients per year. All provide patient information and counseling but their roles in care coordination, patient support and holistic assessment are heterogeneous and not standardized.

Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.

Energy-minimized Scheduling of Real-time Parallel Workflows on Heterogeneous Distributed Computing Systems

Today's large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-recognized that energy consumption accounts for a large part of a computing system's total cost, and timeliness and reliability are two important service indicators. This work studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.

Task Scheduling Algorithm Based on Reliability Perception in Cloud Computing

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods. The model is added into the particle swarm optimization (PSO) algorithm, and an adaptive inertial weighted PSO resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly. When it is 0.4-0.6, the growth rate is relatively slow. When greater than 0.8, the average scheduling length increases sharply, it can be seen that the r-PSO algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase in the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.

An Optimized Weighted Average Makespan in Fault-Tolerant Heterogeneous MPSoCs

The multiprocessor system on chips (MPSoCs) are considered today the core of most modern systems. Most of the applications of these heterogeneous MPSoCs include critical systems and hence terms of fault tolerance and reliability have become essential. Task replication is a technique to carry out fault tolerance and can help for reducing the schedule length by increasing locality. It introduces an upper and lower bound for the makespan of each schedule while each task is replicated more than once. If a fault occurs during execution, the expected makespan will be some value between the upper bound and the lower bound based on when and where the fault has occurred. In this research a new performance parameter namely the weighted average makespan is introduced. It is calculated as the average of the lower and upper bounds of makespan using the probability of occurrence of each. Two scheduling algorithms are presented for fault tolerant scheduling based on directed acyclic graphs. These algorithms are the list scheduling algorithm and the optimizing of the weighted average makespan based on simulated annealing method. The simulation results show that the techniques can improve the schedule length and increase the system reliability without compromising the performance.

SMT-Based Contention-Free Task Mapping and Scheduling on SMART NoC

Networks-on-chips (NoCs) are widely used for on-chip communications in embedded multiprocessor architectures. SMART NoC achieves ultralow latency by enabling single-cycle multiple-hop transmission via bypass channels. However, the contention on the bypass channels seriously degrades the performance of SMART NoC by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this letter, we propose an satisfiability modulo theories (SMTs)-based framework to find the contention-free task mapping with the minimum application schedule length. On top of the SMT’s fast reasoning capability for the conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our approach achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> higher scalability compared to integer linear programming (ILP) approach with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$599.5\times $ </tex-math></inline-formula> faster average runtime for finding an optimum solution.

FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network

Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.