Genetic Algorithm based Cluster Head Selection for Optimimized Communication in Wireless Sensor Network

Wireless Sensor Network (WSNs) utilizes conveyed gadgets sensors for observing physical or natural conditions. It has been given to the steering conventions which may contrast contingent upon the application and system design. Vitality administration in WSN is of incomparable significance for the remotely sent vitality sensor hubs. The hubs can be obliged in the little gatherings called the Clusters. Clustering is done to accomplish the vitality effectiveness and the versatility of the system. Development of the group likewise includes the doling out the part to the hub based on their borders. In this paper, a novel strategy for cluster head selection based on Genetic Algorithm (GA) has been proposed. Every person in the GA populace speaks to a conceivable answer for the issue. Discovering people who are the best proposals to the enhancement issue and join these people into new people is a critical phase of the transformative procedure. The Cluster Head (CH) is picked using the proposed technique Genetic Algorithm based Cluster Head (GACH). The performance of the proposed system GACH has been compared with Particle Swarm Optimization Cluster Head (PSOCH). Simulations have been conducted with 14 wireless sensor nodes scattered around 8 kilometers. Results proves that GACH outperforms than PSOCH in terms of throughput, packet delivery ratio and energy efficiency

Genetic Algorithm based Cluster Head Selection for Optimimized Communication in Wireless Sensor Network

Wireless Sensor Network (WSNs) utilizes conveyed gadgets sensors for observing physical or natural conditions. It has been given to the steering conventions which may contrast contingent upon the application and system design. Vitality administration in WSN is of incomparable significance for the remotely sent vitality sensor hubs. The hubs can be obliged in the little gatherings called the Clusters. Clustering is done to accomplish the vitality effectiveness and the versatility of the system. Development of the group likewise includes the doling out the part to the hub based on their borders. In this paper, a novel strategy for cluster head selection based on Genetic Algorithm (GA) has been proposed. Every person in the GA populace speaks to a conceivable answer for the issue. Discovering people who are the best proposals to the enhancement issue and join these people into new people is a critical phase of the transformative procedure. The Cluster Head (CH) is picked using the proposed technique Genetic Algorithm based Cluster Head (GACH). The performance of the proposed system GACH has been compared with Particle Swarm Optimization Cluster Head (PSOCH). Simulations have been conducted with 14 wireless sensor nodes scattered around 8 kilometers. Results proves that GACH outperforms than PSOCH in terms of throughput, packet delivery ratio and energy efficiency

Szenzor kommunikációs folyamatok állapotadatainak neurális hálózat alapú elemzése: Neural Network Based Analysis of Status Information of Sensor Communication Processes

The Internet of Things requires the communication mechanism to be optimal not only from the data transfer but from the energy consumption point of view, too. One of most analysed types of sensor network is Low Energy Adaptive Clustering Hierarchy (LEACH) system depending on the population density, algorithm of cluster head election, heterogeneity of the energy and physiscal position of the nodes, velocity of the sink node, data aggregation rate and size of data frame. Complexity of the system has been analysed based on status datasets of several hundred simulation cases. The serviceability of LEACH network and dependency properties has been compared with deep learning technics using recurrent neural networks (RNN). Efficient analysis of the Big Data category of status data time series has revealed important behaviour of these sensor networks. This study work is part of PhD research task and project. Kivonat A Tárgyak Internete számos esetben feltételezi, hogy a szenzorok kommunikációs mechanizmusa ne csak az adatátvitel hatékonysága, hanem a továbbításhoz szükséges energia mennyisége szempontjából is optimális legyen. A szenzorhálózatok egyik leginkább elemzett típusa a LEACH (Low Energy Adaptive Clustering Hierarchy) rendszer, amelynek viselkedése olyan paraméterektől függ, mint a populáció sűrűsége, klaszterfej választási algoritmus, csomópontok energiájának, illetve fizikai helyzetének heterogenitása, nyelő csomópont sebessége, adatok tömörítésének mértéke, illetve adatkeretek mérete. A rendszer komplexitását többszáz szimulációs eset által előállított állapotadat halmaz segítségével elemeztem. A LEACH hálózat működését, illetve ennek függőségi jellemzőit a generált idősorok összevetésével dolgoztam fel, amihez visszacsatolásos neurális hálózatra (RNN) épülő mélytanuló technikákat alkalmaztam. A Big Data kategóriájú állapotadat idősorokat tartalmazó halmaz hatékony feldolgozása ezen típusú szenzorhálózatok mély viselkedési jellemzőinek megismerésére adott lehetőséget. Az elemzési tevékenység PhD kutatási munka, illetve kutatási projekt részét képezi

Minimum Bend Shortest Rectilinear Route Discovery for a Moving Sink in a Grid Based Wireless Sensor Network

In a rectilinear route, a moving sink is restricted to travel either horizontally or vertically along the connecting edges. We present a new algorithm that finds the shortest round trip rectilinear route covering the specified nodes in a grid based Wireless Sensor Network.  The proposed algorithm determines the shortest round trip travelling salesman path in a two-dimensional grid graph. A special additional feature of the new path discovery technique is that it selects that path which has the least number of corners (bends) when more than one equal length shortest round trip paths are available. This feature makes the path more suitable for moving objects like Robots, drones and other types of vehicles which carry the moving sink. In the prosed scheme, the grid points are the vertices of the graph and the lines joining the grid points are the edges of the graph. The optimal edge set that forms the target path is determined using the binary integer programming

Minimum Bend Shortest Rectilinear Route Discovery for a Moving Sink in a Grid Based Wireless Sensor Network

In a rectilinear route, a moving sink is restricted to travel either horizontally or vertically along the connecting edges. We present a new algorithm that finds the shortest round trip rectilinear route covering the specified nodes in a grid based Wireless Sensor Network.  The proposed algorithm determines the shortest round trip travelling salesman path in a two-dimensional grid graph. A special additional feature of the new path discovery technique is that it selects that path which has the least number of corners (bends) when more than one equal length shortest round trip paths are available. This feature makes the path more suitable for moving objects like Robots, drones and other types of vehicles which carry the moving sink. In the prosed scheme, the grid points are the vertices of the graph and the lines joining the grid points are the edges of the graph. The optimal edge set that forms the target path is determined using the binary integer programming

Coordinated movement of multiple mobile sinks in a wireless sensor network for improved lifetime

Sink mobility is one of the most effective solutions for improving lifetime and has been widely investigated for the last decade. Algorithms for single-sink mobility are not directly applied to the multiple-sink case due to the latter’s specific challenges. Most of the approaches proposed in the literature use mathematical programming techniques to solve the multiple-sink mobility problem. However, doing so leads to higher complexities when traffic flow information for any possible sink-site combinations is included in the model. In this paper, we propose two algorithms that do not consider all possible sink-site combinations to determine migration points. We first present a centralized movement algorithm that uses an energy-cost matrix for a user-defined threshold number of combinations to coordinate multiple-sink movement. We also give a distributed algorithm that does not use any prior network information and has a low message exchange overhead. Our simulations show that the centralized algorithm gives better network lifetime performance compared to previously proposed MinDiff-RE, random movement, and static-sink algorithms. Our distributed algorithm has a lower network lifetime than centralized algorithms; sinks travel significantly less than in all the other schemes. © 2015, Koç and Korpeoglu

Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput

AbstractIn this paper we consider a link-unreliable remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network, and sensing data by the sensors in the network will be transferred to the remote monitoring center through a third party telecommunication service. A cost associated with this service will be incurred, which will be determined by the number of gateways employed and the cumulative volume of data successfully received within a specified monitoring period. For this scenario, we first formulate a novel constrained optimization problem with an objective to minimize the service cost while a pre-defined network throughput is guaranteed. We refer to this problem as the throughput guaranteed service cost minimization problem and prove that it is NP-complete. We then propose a heuristic for it. The key ingredients of the heuristic include identifying gateways and finding an energy-efficient forest of routing trees rooted at the gateways. We also perform theoretical analysis on the solution obtained. Finally, we conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate the proposed algorithm outperforms other algorithms in terms of both the service cost and the network lifetime

Energy-efficient mobile sink routing scheme for clustered corona-based wireless sensor networks

Wireless Sensor Networks (WSNs) are generally composed of several tiny, inexpensive and self-configured sensor nodes, which are able to communicate with each other via wireless communication devices. The main duty of the nodes is to sense data and transmit to a sink via multi- or single-hop data transmission manners. Since the sensor nodes generally are limited in power resources, they deplete their energy rapidly. In addition, sensor nodes are usually distributed in places, where may be too harsh to be accessible for human. Consequently, exchanging or recharging the power supplies of the sensor nodes is difficult. Therefore, energy efficiency is the most critical issue in design of WSN, which affects the lifetime and performance of the network. Several cluster-based schemes are proposed to enhance the energy efficiency; however, most of them generate sub-optimal clusters without considering both coverage and energy issues simultaneously. Furthermore, several mobility-based schemes are proposed in order to achieve balanced energy consumption through optimizing the sojourn time and sojourn location of Mobile Sinks (MS). Nevertheless, most of them adjust the sojourn time of MS under predictable mobility pattern. Moreover, in most of existing mobility based schemes, time limitation is not considered for optimizing the sojourn location of MS. The aim behind this research is to develop an Energy-efficient Mobile Sink Routing (EMSR) Scheme, which improves the energy efficiency. The EMSR is the incorporation of three schemes: Energyefficient based Unequal-sized Clustering (EUC) mechanism aims to construct the optimal sized clusters, which ensures the energy conservation and coverage preservation. Collaborative Mobile Sink-based Inter-Cluster Routing (CMSICR) mechanism aims to optimize the sojourn time of MS to balance the energy consumption among Cluster Heads (CH). An Energy-efficient Intra-cluster Movement of Mobile Sink (EIM2S) mechanism, which identifies the optimal sojourn locations of the MS within clusters in order to balance the energy consumption among Member Nodes (MN). The EMSR partitions the network field into optimal clusters and employs MSs in order to balance the energy consumption among CHs and MNs. Simulation results show that EMSR achieved improved performance in terms of network lifetime by 51%, total energy consumption by 28% wasted energy by 36% compared to existing schemes. In conclusion, the proposed routing scheme proves to be a viable solution for multi hop cluster based WSN