Wireless Sensor Networks for Smart Homes: A Fuzzy-Based Solution for an Energy-Effective Duty Cycle

Pau, Giovanni; Salerno, Valerio Mario

doi:10.3390/electronics8020131

Cited by 29 publications

(19 citation statements)

References 59 publications

(59 reference statements)

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“…Fuzzy logic-based mechanism utilizing particle swarm optimization algorithm [54] Power consumption reduction and prolonging the lifetime of node and network in industrial wireless sensor networks A fuzzy logic-based algorithm for smart homes [56] Overcome energy consumption in battery nodes in an IEEE 802.15.4 network for smart homes and design a system that can be executed using off-the-shelf hardware Throughput to workload, the battery level of the device, and sleeping time…”

Section: Optimization Of the Sleep/wakeup Mechanismmentioning

confidence: 99%

“…Pau et al [56] proposed a fuzzy logic-based algorithm to overcome the WSN energy consumption problem in battery device nodes in an IEEE 802.15.4 network for smart home applications. Considering the duty cycle, the sleeping time of nodes is dynamically set by the FLC.…”

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confidence: 99%

“…Moreover, as in most of fuzzy-based power management algorithms for WSNs discussed in the 182 literature, the metrics such as network lifetime and battery level were used to evaluate the proposed 183 algorithms in[50][51][52][53][54][55][56]. The authors validated the performance of the proposed technique under 184 different network densities.…”

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confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

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Section: Optimization Of the Sleep/wakeup Mechanismmentioning

confidence: 99%

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confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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“…The authors in [27] proposed the simultaneous use of two fuzzy logic controllers to dynamically adjust the sleeping time and the transmission power of the nodes in order to optimize consumption. We want to highlight [28]: a fuzzy logic-based method that considers the throughput, workload, and battery's level for managing the amount of time the devices of a WSN could be in sleep mode in the domain of the smart home. Three different heuristic optimizations for the duty cycle of nodes were presented in [29] in order to find optimal sensing of different points of interest, overlapping sensing areas among sensors and setting them in sleep mode to improve WSN lifetime.…”

Section: Related Workmentioning

confidence: 99%

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation

Marrero

Suárez

Macías

et al. 2019

Sensors

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Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.Sensors 2020, 20, 30 2 of 22 named end devices (EDs), with different sleep modes. The EDs are programmed to realize a prefixed sampling period, from a few seconds to days or more, and they remain idle until the next sampling period. Conversely, all other devices, like Router Devices (RD) and the Coordinator (C), remain active always. That is, they never are in sleep mode.In our previous work [11][12][13], we proposed a sleep mode of operation for WiFi Access Points (AP) for regular emitted traffic from WiFi terminals obtaining a considerable amount of energy-saving. Despite WSN and WiFi networks having different characteristics, operations, and functions, in this paper, we apply those previous ideas to include the sleep mode of operation in RDs and C when EDs emit traffic periodically. At the beginning of each period of time, the RDs and C enter active mode and find out the WSN spanning tree using any routing algorithm. The leaf nodes of that tree are the EDs, the intermediary nodes are the RDs, and the root is the C. Each RD manages one branch of the tree and can estimate when it will receive data frames from its children nodes (EDs or others RDs in the tree). After that, the RDs will send data frames to the upper-level RD or to the C (at level 0 of the tree). Once received and after sending the data frames, the RDs or the C can pass to sleep mode until the next beginning of the period of time. The enlarging of their battery life corresponds to the amount of time the RDs and C will be in sleep mode. The main objective of this paper is to show, under previous assumptions, if it is possible to obtain a significate enlarging of the battery of ZigBee RDs and C considering commercial devices. Up to our knowledge, we are the first to c...

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“…Nowadays, with the development of precision sensors to monitor physical or environmental conditions, the applications of WSNs have been motivated by various industrial and consumer systems, as well as military usages [1][2][3]. Among the increasing research and applications of WSNs, an important field is target localization and tracking [4,5].…”

Section: Introductionmentioning

confidence: 99%

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

Wang

Zhao

Wang

et al. 2019

Sensors

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In wireless sensor networks (WSNs), the problem of measurement origin uncertainty for observed data has a significant impact on the precision of multi-target tracking. In this paper, a novel algorithm based on least squares support vector machine (LS-SVM) is proposed to classify measurement points for adjacent targets. Extended Kalman filter (EKF) algorithm is firstly adopted to compute the predicted classification line for each sampling period, which will be used to classify sampling points and calculate observed centers of closely moving targets. Then LS-SVM algorithm is utilized to train the classified points and get the best classification line, which will then be the reference classification line for the next sampling period. Finally, the locations of the targets will be precisely estimated by using observed centers based on EKF. A series of simulations validate the feasibility and accuracy of the new algorithm, while the experimental results verify the efficiency and effectiveness of the proposal.

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Wireless Sensor Networks for Smart Homes: A Fuzzy-Based Solution for an Energy-Effective Duty Cycle

Cited by 29 publications

References 59 publications

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

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