scholarly journals Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2791
Author(s):  
Victor Sanchez-Aguero ◽  
Francisco Valera ◽  
Ivan Vidal ◽  
Christian Tipantuña ◽  
Xavier Hesselbach
Keyword(s):  
Heuristic Algorithm ◽  
Optimal Solution ◽  
Exact Result ◽  
Life Time ◽  
Ground Truth ◽  
Battery Life ◽  
Energy Aware ◽  
Mission Duration ◽  
Replacement Procedure ◽  
Multi Uav

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

The Location Problem of Given and Indivisible Different Units

1970 ◽  
Vol 2 (3) ◽  
pp. 341-356
Author(s):  
G. Jándy
Keyword(s):  
Exact Solution ◽  
Heuristic Algorithm ◽  
Network Flow ◽  
Location Problem ◽  
Optimal Solution ◽  
Type Problem ◽  
Distribution Problem ◽  
Enumeration Algorithm ◽  
Weighted Distribution ◽  
Partial Enumeration

In cases where certain simplifications are allowed, the location optimisation of given and indivisible different economic units may be modelled as a bi-value weighted distribution problem. The paper presents a heuristic algorithm for this network-flow-type problem and also a partial enumeration algorithm for deriving the exact solution. But it is also pointed out that an initial sub-optimal solution can quickly be improved with a derivation on a direct line only, if the exact solution is not absolutely essential. A numerical example is used to illustrate the method of derivation on a direct line starting with an upper bound given by a sub-optimal solution.

Communication Trust and Energy-Aware Routing Protocol for WSN Using D-S Theory

2021 ◽  
Vol 13 (4) ◽  
pp. 24-36
Author(s):  
Srinivasan Palanisamy ◽  
Sankar S. ◽  
Ramasubbareddy Somula ◽  
Ganesh Gopal Deverajan
Keyword(s):  
Routing Protocol ◽  
Data Transfer ◽  
Trust Model ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Battery Life ◽  
Delivery Ratio ◽  
Energy Aware ◽  
Decision Making System ◽  
Trust Models

Wireless sensor networks (WSN) deployed in open environments make nodes prone to various security attacks due to their resource constrained nature. The compromised nodes are used to mislead the sensed data and disrupt communication, which can affect the entire decision-making system based on the sensed data. It is also possible to drain the sensor nodes energy and reduce the battery life of the networks. Trust models are the preferred mechanism to secure WSN. In this paper, the authors present communication trust and energy aware (CTEA) routing protocol that make use of the proposed trust model to mitigate the effects of badmouth and energy drain attacks. They use Dempster theory to compute communication trust and also consider the energy metric, to establish the route for data transfer. The simulation result shows that the proposed trust model increases the packet delivery ratio, residual energy, and network lifetime by mitigating the nodes misbehaviour in presence of energy drain and bad mouth attacks.

Energy Efficient Image Compression and Transmission in WSN

2016 ◽  
pp. 67-78
Author(s):  
Wajeeha Aslam ◽  
Muazzam A. Khan ◽  
M. Usman Akram ◽  
Nazar Abbas Saqib ◽  
Seungmin Rho
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Image Compression ◽  
Sensor Network ◽  
Image Data ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Battery Life

Wireless sensor networks are greatly habituated in widespread applications but still yet step behind human intelligence and vision. The main reason is constraints of processing, energy consumptions and communication of image data over the sensor nodes. Wireless sensor network is a cooperative network of nodes called motes. Image compression and transmission over a wide ranged sensor network is an emerging challenge with respect to battery, life time constraints. It reduces communication latency and makes sensor network efficient with respect to energy consumption. In this paper we will have an analysis and comparative look on different image compression techniques in order to reduce computational load, memory requirements and enhance coding speed and image quality. Along with compression, different transmission methods will be discussed and analyzed with respect to energy consumption for better performance in wireless sensor networks.

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

scholarly journals Robust Satellite Scheduling Approach for Dynamic Emergency Tasks

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xuejun Zhai ◽  
Xiaonan Niu ◽  
Hong Tang ◽  
Lixin Wu ◽  
Yonglin Shen
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Optimal Solution ◽  
Simulation Experiments ◽  
Multiobjective Genetic Algorithm ◽  
Scheduling Model ◽  
Rapid Responses ◽  
Earth Observation Satellites ◽  
Satellite Scheduling ◽  
Combined Algorithm

Earth observation satellites play a significant role in rapid responses to emergent events on the Earth’s surface, for example, earthquakes. In this paper, we propose a robust satellite scheduling model to address a sequence of emergency tasks, in which both the profit and robustness of the schedule are simultaneously maximized in each stage. Both the multiobjective genetic algorithm NSGA2 and rule-based heuristic algorithm are employed to obtain solutions of the model. NSGA2 is used to obtain a flexible and highly robust initial schedule. When every set of emergency tasks arrives, a combined algorithm called HA-NSGA2 is used to adjust the initial schedule. The heuristic algorithm (HA) is designed to insert these tasks dynamically to the waiting queue of the initial schedule. Then the multiobjective genetic algorithm NSGA2 is employed to find the optimal solution that has maximum revenue and robustness. Meanwhile, to improve the revenue and resource utilization, we adopt a compact task merging strategy considering the duration of task execution in the heuristic algorithm. Several experiments are used to evaluate the performance of HA-NSGA2. All simulation experiments show that the performance of HA-NSGA2 is significantly improved.

Cost-Effective Energy Management for Hybrid Electric Heavy-Duty Truck Including Battery Aging

2013 ◽  
Author(s):  
T. H. Pham ◽  
P. P. J. van den Bosch ◽  
J. T. B. A. Kessels ◽  
R. G. M. Huisman
Keyword(s):  
Energy Management ◽  
Combustion Engine ◽  
Life Time ◽  
Battery Life ◽  
Heavy Duty ◽  
Energy Management Strategy ◽  
Battery Power ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Power Capability

Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS’s operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.

Thermal Management of Low Profile Applications

2007 ◽  
Author(s):  
Ed Walsh ◽  
Pat Walsh ◽  
Ronan Grimes ◽  
Vanessa Egan
Keyword(s):  
Thermal Management ◽  
Thermal Performance ◽  
Heat Sink ◽  
Optimal Solution ◽  
Battery Life ◽  
Heat Conducting ◽  
Pumping Power ◽  
Low Profile ◽  
Thermal Solution ◽  
Footprint Area

There is an increasing need for low profile thermal management solutions for applications in the range of five to ten watts targeted at portable electronic devices. This need is emerging due to enhanced power dissipation levels in portable electronics. This work focuses upon the optimization of such a solution within certain constraints of profile and footprint area. A number of fan geometries have been investigated where both the inlet and exit rotor angles are varied relative to the heat conducting fins on a heat sink. The ratio of fan diameter to heat sink fin length was also varied. The objective was to determine the optimal solution from a thermal management perspective within defined constraints. The results show good thermal performance for low profile thermal management solutions, and highlight the need to develop the heat sink and fan as an integrated thermal solution rather than in isolation as is the traditional methodology. It is also found that while increasing pumping power generally improves the thermal performance, only small gains are achieved for relatively large pumping power increases. This is important in optimizing portable systems which are powered by limited battery life.

scholarly journals A Chain Oriented Data Collection Protocol for Energy-Aware and Delay-Constrained WSN

2006 ◽  
Vol 2 (3) ◽  
Author(s):  
Nahdia Tabassum ◽  
Quazi Ehsanul Kabir Mamun ◽  
Ahsanul A K M Haque ◽  
Yoshiyori Urano
Keyword(s):  
Routing Protocol ◽  
Optimal Solution ◽  
Energy Awareness ◽  
Energy Aware ◽  
Long Distance ◽  
Available Energy ◽  
Target Field ◽  
Time Requirements ◽  
A Chain ◽  
Maximum Utilization

Energy awareness plays an important role in developing routing protocol for the battery powered wireless sensor networks. As the replacement of the battery is often unfeasible in practical situations, we present here an optimal solution for the maximum utilization of precious available energy at the same time trying to minimize the latency in data delivery. We propose to form hierarchical chains with deployed sensors to collect information from a target field where data get fused at every node level before transmitted finally. Our protocol utilizes the higher energy nodes for more frequent long distance transmissions so that the energy expenditure become even between all nodes in the network irrespective of their physical locations. It has been found in our simulation that this protocol outperforms other hierarchical protocols like LEACH and PEGASIS in both the cases of energy consumption and time requirements respectively. It has been also found that the overall lifetime of the sensor network also increases in our protocol.

Sign in / Sign up

Export Citation Format

Share Document