Energy Management in Wireless Sensor Network Using PEGASIS

Mahakud, Rina; Rath, Satyanarayan; Samantaray, Minu; Sinha, BabySradha; Priya, Priyanka; Nayak, A.; Kumari, Aarti

doi:10.1016/j.procs.2016.07.347

Cited by 14 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The one-diode model describes the parameters of the solar cells where 𝑖 represents the source photon current generated by the PV effect. The total net current 𝑖 can be calculated as in (6) where 𝐼 is the reverse saturation current, 𝐼 is the shunt current, T is the absolute temperature, q is the charge of the electron, k is the Boltzmann constant, 𝑅 is the shunt resistor, 𝑣 is the voltage difference across the PV cell terminals, and 𝛼 is the diode factor [33,34]. The efficiency of the solar cell 𝜂 is related to the ratio between the input power and the maximum output power.…”

Section: Pv Cellsmentioning

confidence: 99%

“…Power management in WSN devices is always a challenging issue. Such devices are required to operate in remote locations for several years without charging or replacing the storage element, compared with other battery-powered devices like mobile phones [6,7]. Battery management techniques, low power MCUs and low power communication technologies are required to mitigate the power consumption in WSN systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Self-Powered WSN: The Design of Ultra-Low-Power Wireless Sensor Transmission Unit Based on Indoor Solar Energy Harvester

Haroun¹,

Deros²,

Alkahtani³

et al. 2022

Preprint

View full text Add to dashboard Cite

The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The power consumption of the developed WSTx was 21.09µW during the sleep mode and 11.1mW during the operation mode. The harvesting system can harvest energy up to 1.2mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%.

show abstract

Section: Pv Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Self-Powered WSN: The Design of Ultra-Low-Power Wireless Sensor Transmission Unit Based on Indoor Solar Energy Harvester

Haroun¹,

Deros²,

Alkahtani³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…FLS consists of four key components: fuzzier, inference engine, fuzzy rules, and defuzzifier, as shown in Figure 3 [32]. if SN receives the broadcasting message then (4) if Message is Level-Msg then (5) if routing level of node != 0 then (6) if routing level of node > routing level within received message then (7) Update routing level of SN (8) if Node is CH then (9) routing level ← routing level + 1 (10) Broadcast new Level-Msg (ID, routing level) (11) end if (12) end if (13) Else (14) Update routing level of SN with routing level within received message (15) end if (16) else if Message is Level-Request-Msg then (17) if routing level of node != 0 then (18) Send The fuzzifier gets crisp inputs and converts them to a fuzzy set, which is represented by a linguistic term, such as "near," "medium," or "far," using a membership function. This process is known as fuzzification.…”

Section: Fuzzy Logic Systemmentioning

confidence: 99%

“…Typically, a CN captures the multimedia content and sends it to an NN for processing or transmission to its base station (BS). Although there are various practical transmission schemes [6][7][8][9], a hierarchical structure is typically applied, such as a cluster-based approach [10], which has the key advantages of direct-transmission energy optimization and zoning, which prolong the network lifetime. Figure 1 shows an example of SN deployment; here, there are two possible clusters: (1) a transmission cluster of the NN (here, its representation as cluster head is included) and (2) a camera cluster.…”

Section: Introductionmentioning

confidence: 99%

Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

Heng

So-In

Nguyen

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.

show abstract

“…A wireless sensor network (WSN) consists of sensor nodes to examine and monitor environmental factors such as temperature, sound etc. and send their data to the base station as discussed in [1] [2] [3]. Wireless Body Area Network (WBAN) is a special application of WSN to monitor variables remotely.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks

Mile¹,

Okeyo²,

Kibe³

2018

JST

View full text Add to dashboard Cite

Due to the development in the field of Wireless Sensor Networks (WSNs), its major application, Wireless Body Area Network (WBAN) has presently become a major area of interest for the developers and researchers. Efficient sensor nodes data collection is the key feature of any effective wireless body area network. Prioritizing nodes and cluster head selection schemes plays an important role in WBAN. Human body exhibits postural mobility which affects distances and connections between different sensor nodes. In this context, we propose maximum consensus based cluster head selection scheme, which allows cluster head selection by using Link State. Nodal priority through transmission power is also introduced to make WBAN more effective. This scheme results in reduced mean power consumption and also reduces network delay. A comparison with IEEE 802.15.6 based CSMA/CA protocol with different locations of cluster head is presented in this paper. These results show that our proposed scheme outperforms Random Cluster head selection, Fixed Cluster head at head, Foot and Belly positions in terms of mean power consumption, network delay, network throughput and bandwidth efficiency.

show abstract

Energy Management in Wireless Sensor Network Using PEGASIS

Cited by 14 publications

References 4 publications

Towards Self-Powered WSN: The Design of Ultra-Low-Power Wireless Sensor Transmission Unit Based on Indoor Solar Energy Harvester

Towards Self-Powered WSN: The Design of Ultra-Low-Power Wireless Sensor Transmission Unit Based on Indoor Solar Energy Harvester

Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks

Contact Info

Product

Resources

About