Energy efficient policy selection in wireless sensor network using cross layer approach

Trapasiya, Samir; Soni, Himanshu

doi:10.1049/iet-wss.2017.0027

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…So energy efficient MAC protocol plays a vital role in lifetime of WSN. Time division multiple access (TDMA) and Code division multiple access (CDMA) are appropriate one to avoid energy consumption at link layer In the research work [14], it is found that sleep synchronization among nodes affects energy consumption. Since the listening time in the MAC layer has to influence in relay delay and delivery ratio, the degree of sleep coordination between nodes yields the corresponding power consumption.…”

Section: Mac Layermentioning

confidence: 99%

“…• In [14], to produce synchronic and dynamic adaptive listening scheme author jointly considers listening time in MAC layer and routing scheme in the network layer, along with the channel information from the physical layer. The forward delay and energy consumption are reduced due to the coordination between nodes in terms of their sleeping time.…”

Section: Possible Solution To Increase Energy Efficiency In Wsnmentioning

confidence: 99%

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Energy Efficient MAC Protocol for Heterogeneous Wireless Sensor Network using Cross-Layer Design

Dhage*,

Vemuru

2019

IJRTE

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Over the last decade, Wireless Sensor Network (WSN) has gained considerable attention in various real-time applications. Since WSN works with battery operated nodes, utilization and optimization of node energy is one the primary challenge. For effectively handling and controlling the energy consumption problem in WSN, cross-layer optimization is one of the important methods. For a researcher working in the domain of WSN, energy constraint is a huge challenge to deal with. MAC layer is one of the major source of energy consumption so, an innovative energy efficient MAC protocol using a cross-layer approach in the heterogeneous wireless sensor network is proposed. In this protocol, first, packet retransmission by reducing packet loss is addressed by considering buffer space, channel state and remaining energy. Second, the synchronization scheme for a global schedule is implemented by deliberating adaptive listening using the length of the transmission queue. Finally, sleep time issue is worked out to reduce energy consumption. In this scenario, the nodes will be in sleep mode unless it has some packets to send or receive. The proposed protocol is implemented in Network Simulation (NS2). The simulation results show that the heterogeneous wireless network performs better in terms of energy consumption, packet data rate and energy buffer state while implementing through the proposed protocol.

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Section: Mac Layermentioning

confidence: 99%

Section: Possible Solution To Increase Energy Efficiency In Wsnmentioning

confidence: 99%

Energy Efficient MAC Protocol for Heterogeneous Wireless Sensor Network using Cross-Layer Design

Dhage*,

Vemuru

2019

IJRTE

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“…Let C g = c g = 0.5, C p = C g + c p = 0.5 + 0.2 = 0.7, C w = C p + c w = 0.7 + 0.25 = 0.95, X 10,8 = (1.1, 1.8, 3.5, 2.2, 0.1) which is the 8th solution of the 11th generation, P 8 = (2.5, 2.0, 1.2, 1.9, 5.0), and P gBest = (3.3, 2.8, 1.2, 4.5, 5.6). An example is shown below to explain how SSO is implemented to update X 10,8 to X 11,8 in Table 2. The lower bound l i , the upper-bound u i , and the value of ρ i for variable x 10,8,i are listed in the 2nd, 3rd and 7th row of Table 2, respectively.…”

Section: The Sso and An Examplementioning

confidence: 99%

“…Mekonnen et al [2] proposed a prototype of a WSN applied in a video surveillance system to optimize energy consumption. Trapasiya and Soni [11] addressed the goal of retransmission energy reduction in WSNs. Quang and Kim [12] proposed a gradient routing in an industrial WSN to optimize energy consumption.…”

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confidence: 99%

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

et al. 2018

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Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.Energies 2018, 11, 2385 2 of 23 the WSN system fails because nodes deplete their limited battery power. In other words, a critical restriction in any WSN system is energy consumption. Therefore, numerous investigations of WSNs have had a primary focus on energy consumption.Optimizing energy efficiency (EE) in a WSN, which is defined as the ratio of output over energy consumption, has been the subject of a great number of studies. Mekonnen et al. [2] proposed a prototype of a WSN applied in a video surveillance system to optimize energy consumption. Trapasiya and Soni [11] addressed the goal of retransmission energy reduction in WSNs. Quang and Kim [12] proposed a gradient routing in an industrial WSN to optimize energy consumption. Setiawan et al. [13] came up with an energy management policy to maximize energy transfer efficiency for a WSN. Liu et al. [14] optimally designed a WSN to minimize energy consumption.The minimization of energy consumption in WSNs has also been investigated by many researches [15][16][17][18]. Chanak et al. [19] discussed the balance of energy consumption among deployed sensors in a WSN. The energy-optimal routing problem in WSNs has recently been studied by several works [20][21][22]. The improvement of energy consumption by clustering method in a WSN has also been discussed by some works [23,24].However, the measured values of energy consumption in real-life WSN systems are usually uncertain and imprecise. Fuzzy set theory can effectively resolve these uncertain and imprecise problems. In studies of energy consumption for WSNs, few researchers have presented fuzzy-based methods to solve uncertainty problems in such systems. Collotta et al. [25] considered the turning on/off of devices as the output of a Fuzz...

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“…Xia Weichao [8] deeply analyzed the relationship between satisfying customer needs and supplier management and believed that category management should be based on category accounting, which is designed to meet consumer needs and manage suppliers' new management methods. Wu Zikai [9] emphasized the importance of category tactics.…”

Section: Introductionmentioning

confidence: 99%

Market segmentation of wireless sensor system in network commodity selection

Liu

2019

J Wireless Com Network

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Online product recommendation and market segmentation are directly related to the consumer experience and the healthy development of the e-commerce market. In order to improve the selection of network products and the effect of market segmentation, this study introduced a wireless sensing system based on the recommendation of traditional network products and combined the operational conditions of the network commodity market to construct a personalized recommendation e-commerce system. Meanwhile, a series of representative WSN congestion control strategies are designed by dynamically adjusting the network topology, routing algorithm, channel allocation protocol, and channel resource utilization, and the system structural components and functions are implemented. The research shows that the proposed algorithm and system have certain practical effects and can provide theoretical reference for subsequent related research.

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Energy efficient policy selection in wireless sensor network using cross layer approach

Cited by 17 publications

References 23 publications

Energy Efficient MAC Protocol for Heterogeneous Wireless Sensor Network using Cross-Layer Design

Energy Efficient MAC Protocol for Heterogeneous Wireless Sensor Network using Cross-Layer Design

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

Market segmentation of wireless sensor system in network commodity selection

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