Deployment of Multiple Sink in the different Monitoring Area based on Optimization Technique

2019 ◽  
Vol 09 ◽  
Author(s):  
Sonal Telang Chandel ◽  
Sanjay Sharma
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Optimization Technique ◽  
Network Connectivity ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Multiple Sinks ◽  
Full Coverage ◽  
Monitoring Area

Wireless Sensor Networks are widely used in different applications like environmental monitoring, health monitoring, wildlife monitoring, etc. The monitored area may be of any shape such as circular, rectangular, and square. Finding an ideal node deployment technique in wireless sensor systems (WSNs) that would diminish cost, be powerful to node failure, shorten calculation, and communication overhead, and guarantee full coverage alongside network connectivity is a troublesome issue. Indeed, sensing coverage and system connectivity are two of the most basic issues in WSNs as they can straightforwardly affect the system lifetime and activity. In traditional WSNs, a single sink is deployed which results in more traffic load on that sink cause higher energy consumption. Thus, it is necessary to deploy multiple sinks. The efficient deployment of sensors and multiple sinks is a challenging task as the performance of the network is depending on it. In this paper, sensors and multiple sinks are deployed using SSDOA (Sensor Sink Deployment Optimization Algorithm) in a different monitoring area. Performance parameters like coverage, network lifetime, energy consumption are analyzed. Compared to existing methods our method performs better in any type of monitoring area. Reported numerical results show that our method outperforms PSO, GA and Random deployment in the square monitoring area with 9% better network lifetime, 4% full coverage and 7.3% lesser energy consumption respectively. Furthermore, our proposed approach also performs better in the circular and rectangular monitoring area.

GRPW-MuS: Geographic Routing to Multiple Sinks in Connected Wireless Sensor Networks

2016 ◽  
Vol 4 (3) ◽  
pp. 486
Author(s):  
Yassine Sabri
Keyword(s):  
Energy Consumption ◽  
Life Time ◽  
Research Problem ◽  
Geographic Routing ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Protection Scheme ◽  
Mobile Sinks ◽  
Multiple Sinks ◽  
Multiple Sink

Multiple sinks routing is envisioned as a possible solution to the bottleneck research problem in Wireless Sensor Networ ks (WSN). In addition to focusing on minimizing the energy consumption in a WSN, it is also equally impor tant to design routing protocols that fair ly and evenly distr ibute the networ k traffic; in order to prolong the networ k life time and improve its scalability.In this paper we present an enhancement to the GRPW algor ithm for wireless sensor networ ks. Perfor mance of GRPW algor ithm algor ithm depends heavily on single sink position , we propose a protocol called GRPW-MuS ( Geographic Routing to Multiple Sinks in connected wireless sensor networ ks) based on Multiple Static Sinks, we modified the existing sink location pr ivacy protection scheme by dividing nodes in the networ k containing multiple sink into different levels in which real packets are forwarded to sink belong to corresponding logical levels and the inter mediate node generating fake packets and sending it to fake sinks. Using OMNET++ simulation and the MiXiM framewor k, it is shown that proposed protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

scholarly journals Mobile Base Station and Clustering to Maximize Network Lifetime in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Oday Jerew ◽  
Kim Blackmore ◽  
Weifa Liang
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Packet Loss ◽  
Network Size ◽  
Base Station ◽  
Sensor Nodes ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Mobile Base Station ◽  
Mobile Base

Using a mobile base station (BS) in a wireless sensor network can alleviate nonuniform energy consumption among sensor nodes and accommodate partitioned networks. In the work of Jerew and Liang (2009) we have proposed a novel clustering-based heuristic algorithm for finding a trajectory of the mobile BS that strikes a nontrivial tradeoff between the traffic load among sensor nodes and the tour time constraint of the mobile BS. In this paper, we first show how to choose the number of clusters to ensure there is no packet loss as the BS moves between clusters. We then provide an analytical solution to the problem in terms of the speed of the mobile BS. We also provide analytical estimates of the unavoidable packet loss as the network size increases. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm. The results show that the use of clustering in conjunction with a mobile BS for data gathering can significantly prolong network lifetime and balance energy consumption of sensor nodes.

QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 43-52
Author(s):  
Omkar Singh ◽  
Vinay Rishiwal
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Life Time ◽  
Base Station ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
End To End

Background & Objective: Wireless Sensor Network (WSN) consist of huge number of tiny senor nodes. WSN collects environmental data and sends to the base station through multi-hop wireless communication. QoS is the salient aspect in wireless sensor networks that satisfies end-to-end QoS requirement on different parameters such as energy, network lifetime, packets delivery ratio and delay. Among them Energy consumption is the most important and challenging factor in WSN, since the senor nodes are made by battery reserved that tends towards life time of sensor networks. Methods: In this work an Improve-Energy Aware Multi-hop Multi-path Hierarchy (I-EAMMH) QoS based routing approach has been proposed and evaluated that reduces energy consumption and delivers data packets within time by selecting optimum cost path among discovered routes which extends network life time. Results and Conclusion: Simulation has been done in MATLAB on varying number of rounds 400- 2000 to checked the performance of proposed approach. I-EAMMH is compared with existing routing protocols namely EAMMH and LEACH and performs better in terms of end-to-end-delay, packet delivery ratio, as well as reduces the energy consumption 13%-19% and prolongs network lifetime 9%- 14%.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

scholarly journals Stable routing and energy-conserved data transmission over wireless sensor networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Suzan Shukry
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Data Transmission ◽  
Source Node ◽  
Wireless Sensor ◽  
Dominant Factor ◽  
Communication Overhead ◽  
Forwarding Node ◽  
Stable Path ◽  
Stable Routing

AbstractStable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing ($$ENSR$$ ENSR ) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. $$ENSR$$ ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality ($$SBC$$ SBC ) is introduced. In $$ENSR$$ ENSR , at first, some nodes will be selected as the stable forwarding nodes, usually with maximum $$SBC$$ SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including $$SBC$$ SBC , to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric ($$SPRM$$ SPRM ). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing ($$GLRR$$ GLRR ) and reliable energy-aware routing protocol $$(RER)$$ ( R E R ) , in terms of network efficiency and reliability.

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

An Energy-Balance Based Distributed Topology Control Algorithm for Wireless Sensor Networks

2012 ◽  
Vol 490-495 ◽  
pp. 1392-1396 ◽  
Author(s):  
Chu Hang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Topology Control ◽  
Control Algorithm ◽  
Path Loss ◽  
Network Connectivity ◽  
Premature Death ◽  
Residual Energy ◽  
Wireless Sensor

Topology control is an efficient approach which can reduce energy consumption for wireless sensor networks, and the current algorithms mostly focus on reducing the nodes’ energy consumption by power adjusting, but pay little attention to balance energy consumption of the whole network, which results in premature death of many nodes. Thus, a distributed topology control algorithm based on path-loss and residual energy (PRTC) is designed in this paper. This algorithm not only maintains the least loss links between nodes but also balances the energy consumption of the network. The simulation results show that the topology constructed by PRTC can preserve network connectivity as well as extend the lifetime of the network and provide good performance of energy consumption.

Prolonging network lifetime and optimizing energy consumption using swarm optimization in mobile wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 534-541
Author(s):  
Sangeetha M. ◽  
Sabari A.
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Mobile Wireless ◽  
Swarm Optimization ◽  
Content Type ◽  
Mobile Wireless Sensor ◽  
Practical Implications

Purpose This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique. Design/methodology/approach In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs. Findings Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively. Research limitations/implications In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only. Practical implications To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m2 network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s. Practical implications This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption. Originality/value PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.

Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks

2020 ◽  
pp. 33-46
Author(s):  
A. Sariga ◽  
◽  
◽  
J. Uthayakumar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Hot Spot ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Clustering Protocols ◽  
Hot Spot Problem

Wireless sensor network (WSN) is an integral part of IoT and Maximizing the network lifetime is a challenging task. Clustering is the most popular energy efficient technique which leads to increased lifetime stability and reduced energy consumption. Though clustering offers several advantages, it eventually raises the burden of CHs located in proximity to the Base Station (BS) in multi-hop data transmission which makes the CHs near BS die earlier than other CHs. This issue is termed as hot spot problem and unequal clustering protocols were introduced to handle it. Presently, some of the clustering protocols are developed using Type-2 Fuzzy Logic (T2FL) but none of them addresses hot spot problem. This paper presents a Type-2 Fuzzy Logic based Unequal Clustering Algorithm (T2FLUCA) for the elimination of hot spot problem and also for lifetime maximization of WSN. The proposed algorithm uses residual energy, distance to BS and node degree as input to T2FL to determine the probability of becoming CHs (PCH) and cluster size. For experimentation, T2FLUCA is tested on three different scenarios and the obtained results are compared with LEACH, TEEN, DEEC and EAUCF in terms of network lifetime, throughput and average energy consumption. The experimental results ensure that T2FLUCA outperforms state of art methods in a significant way.

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