scholarly journals A Genetic Algorithm Based Minimal Maximal Load Tree Routing Algorithm in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhi Huang
Keyword(s):  
Genetic Algorithm ◽  
Network Lifetime ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Optimal Routing ◽  
Energy Efficient Routing ◽  
Maximal Load ◽  
The Individual ◽  
Routing Tree ◽  
Static Routing

The key issue of static routing algorithms is how to construct an energy efficient routing tree that is utilized during the whole network duration in order to extend network lifetime. In this paper, we have illuminated that, in applications that define network lifetime as the time when the first sensor dies, the optimal routing tree should be the routing tree with minimal maximal load of all sensors and named such trees the Minimal Maximal Load Tree (MMLT). Since the procedure of constructing a routing tree is complex and the number of possible routing trees in a network is very huge, we have proposed a genetic algorithm (GA) based algorithm to obtain approximate Minimal Maximal Load Tree (MMLT). Each individual corresponds to a routing tree, and the fitness function is defined as the maximal load of all sensors in accordance with the routing tree that the individual corresponds to. Thus, approximate MMLT is obtained and network lifetime is extended. Simulation results show that our proposed algorithm notably extends network lifetime.

scholarly journals A Genetic Algorithm Based and Subtree Restricted Routing Algorithm in Wireless Sensor Networks

2018 ◽  
Vol 14 (03) ◽  
pp. 52
Author(s):  
Zhi Huang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Lower Layer ◽  
Routing Algorithm ◽  
Search Space ◽  
Wireless Sensor ◽  
Optimal Routing

<span lang="EN-US">The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load</span><span lang="EN-US"> thus extend network lifetime in wireless sensor networks. However,  t</span><span lang="EN-US">he existing algorithm</span><span lang="EN-US">limits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent.</span><span lang="EN-US"> In this paper, w</span><span lang="EN-US">e have found that how <a name="OLE_LINK26"></a><a name="OLE_LINK25"></a>cycles are generated during GA operations</span><span lang="EN-US">, and </span><span lang="EN-US">proposed a solution based on subtree to avoid generating cycles during GA operations.</span><span lang="EN-US">The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended.</span><span lang="EN-US"> The experiment shows that our proposed algorithm extend network lifetime notably.</span>

scholarly journals Application of the genetic algorithm to the task of compiling the curriculum

2018 ◽  
Author(s):  
V. A. Turchina ◽  
D. O. Tanasienko
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Necessary Conditions ◽  
Fitness Function ◽  
Educational Process ◽  
Heuristic Methods ◽  
Educational Groups ◽  
The Individual ◽  
Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

scholarly journals Exponential Genetic Algorithm-Based Stable and Load-Aware QoS Routing Protocol for MANET

2017 ◽  
Vol 26 (4) ◽  
pp. 669-682
Author(s):  
A. Rama Rao ◽  
V. Valli Kumari ◽  
Ch. Satyananda Reddy
Keyword(s):  
Genetic Algorithm ◽  
Exponential Function ◽  
Routing Protocol ◽  
Ad Hoc ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Qos Routing ◽  
Multimedia Data ◽  
Link Quality ◽  
Delivery Ratio

AbstractAs the actual links in mobile ad hoc networks (MANETs) are dynamic, the feasible path generated may not exist during the transmission of multimedia data. There is a need for generating multiple paths for guaranteed transmission. The multi-path finding can be achieved through a genetic-based algorithm that faces the major challenge in quality-of-service (QoS)-aware routing. To enhance the QoS communication over the MANETs, an exponential genetic algorithm (GA)-based stable and load-aware QoS routing protocol (SLAQR) is proposed in this paper. This paper chiefly focuses on the enhancement of the GA-based routing algorithm by including exponential function in the searching process and modifying the fitness function, which incorporates QoS metrics like the node’s static resource capacity, dynamic resource availability, neighborhood quality, and link quality. The originality of the proposed protocol comes from the fact that it introduces multiple parameters into the route quality computation and integrates the exponential function into the GA. For experimental validation, the simulation of the proposed method is done and the results are compared with existing protocols such as GAQR (GA-Based QoS Routing), QMRB-AODV (QoS Mobile Routing Backbone Over Ad Hoc On-Demand Distance Vector Routing), and EISGA (Ensemble of Immigrant Strategies with Genetic Algorithm). From the outcome, we conclude that the delivery ratio, throughput, and delay of our proposed SLAQR approach have improved the GAQR approach by 58%, 56%, and 97%, respectively.

Hardware Implementation of a Genetic Algorithm for Motion Path Planning

2016 ◽  
pp. 250-275
Author(s):  
Imbaby I. Mahmoud ◽  
May Salama ◽  
Asmaa Abd El Tawab Abd El Hamid
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Fitness Function ◽  
Random Number Generation ◽  
Motion Path ◽  
Genetic Operators ◽  
Fitness Evaluation ◽  
Evaluation Task ◽  
Evaluation Module ◽  
The Individual

The aim of this chapter is to investigate the hardware (H/W) implementation of Genetic Algorithm (GA) based motion path planning of robot. The potential benefit of using H/W implementation of genetic algorithm is that it allows the use of huge parallelism which is suited to random number generation, crossover, mutation and fitness evaluation. The operation of selection and reproduction are basically problem independent and involve basic string manipulation tasks. The fitness evaluation task, which is problem dependent, however proves a major difficulty in H/W implementation. Another difficulty comes from that designs can only be used for the individual problem their fitness function represents. Therefore, in this work the genetic operators are implemented in H/W, while the fitness evaluation module is implemented in software (S/W). This allows a mixed hardware/software approach to address both generality and acceleration. Moreover, a simple H/W implementation for fitness evaluation of robot motion path planning problem is discussed.

scholarly journals Prolonging the Lifetime of Wireless Sensor Networks Interconnected to Fixed Network Using Hierarchical Energy Tree Based Routing Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
M. Kalpana ◽  
R. Dhanalakshmi ◽  
P. Parthiban
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Research Work ◽  
Product Evaluation ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Data Packets ◽  
Fixed Network

This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.

scholarly journals Multipath Routing Based on Genetic Algorithm in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Shuang Wang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Multipath Routing ◽  
Base Station ◽  
Wireless Sensor ◽  
Routing Scheme

The two most important factors that must be considered in the wireless sensor networks are energy efficiency and fault tolerance. Multipath routing is an effective method to improve the fault tolerance in wireless sensor networks. By taking the energy consumption into consideration, in this paper, a multipath routing algorithm for the wireless sensor networks based on the genetic algorithm is proposed. The proposed algorithm computes the fitness function by using the distance between nodes in the network and then generates the routing scheme at the base station. The routing scheme is shared with all the nodes of the entire network, to realize the multipath routing for each node. Finally, the simulation experiment is used to verify the validity of our method, and the results show that the routing method in this paper has a better effect.

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