Optimized sink node path using particle swarm optimization

Mendis, Champake; Guru, Siddeswara Mayura; Halgamuge, Saman K.; Fernando, Saman

doi:10.1109/aina.2006.254

Cited by 24 publications

(13 citation statements)

References 9 publications

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“…The initial energy of each node is 100J and two ways ground propagation method is used with Omni antenna. Careful selection of the Base Station's location in a Wireless Sensor Network may affect various performance metrics such as energy consumption, delay, packet delivery ratio and throughput [4,5]. The parameters used in simulation for LEACH operation is shown in Table 2.…”

Section: Simulation Environmentsmentioning

confidence: 99%

Impact of Base Station Location on Wireless Sensor Networks

Koyi

Yang

Kwon

2015

Advances in Intelligent Systems and Computing

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Abstract. Wireless sensor networks (WSNs) have attracted much attention in recent years due to their potential use in many applications such as surveillance, militant etc. Given the importance of such applications, maintaining a dependable operation of the network is a fundamental objective. In Wireless Sensor Networks, many algorithms have been devised to improve energy maintenance in a whole network. Most of them assume the location of the Base Station (BS) to be at the border of the network even though location of Base Station takes some role in overall performance of the network. So in this paper, we simulated WSN performance in energy consumption, throughput, packets delivery ratio and delay with different locations of BS. Simulation results showed the best performance when a Base Station is located in the center of the WSN field and the worst when a Base Station is in the corner of the WSN. Compared to the existing location assumption, with the best positioned BS, Cluster Heads consumed 69% and with the worst positioned BS, they consumed 127% in energy. When we build WSN, if we spend some higher cost for installing BS inside the network, its overall performance can be improved much.

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Section: Simulation Environmentsmentioning

confidence: 99%

Impact of Base Station Location on Wireless Sensor Networks

Koyi

Yang

Kwon

2015

Advances in Intelligent Systems and Computing

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“…The velocity of the MA ranges between (1-10 m/s). The MA trajectory is optimized using particle swarm optimization (PSO) [25,26]. PSO is an optimization technique introduced to solve problems for which we do not have polynomial time algorithms [27], so far.…”

Section: Related Workmentioning

confidence: 99%

“…The BS is designated to be at midpoint of the network field, i.e., (50,50), see Table 1. In addition, we use the energy model [25] to compute the energy consumption due to communication among nodes. We presume that each SN creates one data packet per time unit and transmits it to its CH.…”

Section: Research Articlementioning

confidence: 99%

MA-LEACH: Energy Efficient Routing Protocol for WSNs using Particle Swarm Optimization and Mobile Aggregator

Elsayed¹,

Sharaf²

2018

ijcna

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-Routing protocols for wireless sensor networks pay a great attention to the limited resources of the nodes. As battery lifetime is a major concern, we consider our proposed model as an effort to prolong the network lifetime in harmony with WSNs' constraints. In this paper, we study LEACH routing protocol and its performance and propose an extension to it, MA-LEACH. We introduce a mobile aggregator (MA) which is a gadget adopted to mitigate the overhead on the cluster heads (CHs). In addition, we optimize the trajectory using particle swarm optimization (PSO). Hence, we adapt the TSP problem to our protocol to determine the optimal trajectory that a mobile aggregator could travel to visit every cluster head in the network. We simulate the proposed protocol in MATLAB and the results reveal that it outperforms LEACH in network lifetime and energy consumption. Also, we compare our findings with a recent extension to the LEACH called LEACH with fuzzy descriptors and with clustered heterogeneous sensor networks (CHSNs) with a mobile sink. The simulation results show that MA-LEACH surpasses LEACH with fuzzy descriptor and CHSNs.

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“…Each particle moves toward best optimal solutions with every iterations by learning from past experiences and surroundings. Due to its simplicity, high convergence rate, and searching capability, PSO has been deployed to many optimization problems [12]. After achieving the required output or number of iterations, process terminates.…”

Section: Ivparticle Swarm Optimizationmentioning

confidence: 99%

Throughput Optimization in Cooperative Communications using Evolutionary Algorithms

Singh¹,

Raman²,

Kakkar³

2014

IJAREEIE

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This paper illustrates the results of the throughput of Secondary User (SU) in cognitive radios under cooperative scenario with the use of evolutionary algorithms in the presence of additive white Gaussian (AWGN) noise. In this work, the performance of half-voting and OR fusion rule is studied in terms of P d Vs P f curves. OR fusion rule is found to be most suitable in cooperative scenario. Then, Particle Swarm Optimization (PSO) and Biogeography Based Optimization (BBO) algorithms are implemented using OR fusion rule to enhance the throughput of cognitive users by taking in to account the protection of primary (licensed) users. The probability of detection is set to 0.9 for protection purpose. Simulations results in terms of co-operations shows that PSO performs better initially but as numbers of co-operations increases PSO performance degrades. KEYWORDS: Throughput, Evolutionary Algorithms, Fusion rules, Optimization I.INTRODUCTIONIn recent years, cognitive radio (CR) has emerged as a promising paradigm for exploiting the spectrum opportunity, which is restricted by the current rigid spectrum allocation scheme, to solve the spectrum scarcity problem [1][2]. Different spectrum sensing techniques are available for sensing primary users (PUs) signals. They can be broadly classified in to three categories: matched filter detection, cyclostationary detection and energy detection [19]. Energy detection is widely adopted because of lower complexity than other two schemes and priori knowledge of signal is also not required. Spectrum sensing depends upon two probabilities namely detection probability (P d ) and false alarm probability (P f ) [5]. Detection probability is related to probability of correctly detecting the presence of PUs, whereas false alarm probability is related to incorrect detection of PUs when it is absent [4]. Many research works are proposed to improve the performance metrics of detection probability and false alarm probability. However, PSO is addressed for sensing-throughput trade-offs problem under cooperative scenarios using various hard combination fusion schemes [3]. Optimization performance of cooperative sensing in terms of number of cooperative users is performed using constant detection rate (CDR) and constant false alarm rate [4]. But, there is no consideration for throughput of CUs. The work in [5] focuses on the optimization of sensing-throughput tradeoffs using iterative algorithms. In this paper, PSO and BBO algorithms are implemented to achieve the same objective. Both the algorithms are performed over OR fusion schemes.The Paper is organized as follows: Section-II denotes the system model and other performance metrics. A BBO and PSO algorithms are discussed in section III and IV respectively. Section 5 covers the methodology used by proposed work. However, the section VI and section VII include results and conclusions respectively. II.SYSTEM MODELThis study focuses on PUs signals with AWGN at CUs. We assume that both noise and PUs signal are independent. The basic model...

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Optimized sink node path using particle swarm optimization

Cited by 24 publications

References 9 publications

Impact of Base Station Location on Wireless Sensor Networks

Impact of Base Station Location on Wireless Sensor Networks

MA-LEACH: Energy Efficient Routing Protocol for WSNs using Particle Swarm Optimization and Mobile Aggregator

Throughput Optimization in Cooperative Communications using Evolutionary Algorithms

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