Energy Efficient MAC Protocols

Hayat, S.A.; Javaid, Nadeem; Khan, Zahoor Ali; Shareef, Ali; Mahmood, Asad; Bouk, Safdar Hussain

doi:10.1109/hpcc.2012.174

Cited by 20 publications

(10 citation statements)

References 13 publications

(27 reference statements)

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“…With such an adaptation, it becomes possible to set automatically a platform in a deep-sleep mode whenever it is not used for a significant time. At the network level, power management optimizations are often made in medium access protocols (MAC), routing protocols and transport protocols [24]. At the routing and transport layers, we can observe that many energy efficient protocols have been proposed for WSNs [25,26,27].…”

Section: On the Positive Influence Of Automated Sampling On Power Conmentioning

confidence: 99%

Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

Cecchinel¹,

Fouquet²,

Mosser³

et al. 2019

Future Generation Computer Systems

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Sensor networks empower Internet of Things (IoT) applications by connecting them to physical world measurements. However, the necessary use of limited bandwidth networks and battery-powered devices makes their optimal configuration challenging. An over-usage of periodic sensors (i.e. too frequent measurements) may easily lead to network congestion or battery drain effects, and conversely, a lower usage is likely to cause poor measurement quality. In this paper we propose a middleware that continuously generates and exposes to the sensor network an energy-efficient sensors configuration based on data live observations. Using a live learning process, our contributions dynamically act on two configuration points: (i) sensors sampling frequency, which is optimized based on machine-learning predictability from previous measurements, (ii) network usage optimization according to the frequency of requests from deployed software applications. As a major outcome, we obtain a self-adaptive platform with an extended sensors battery life while ensuring a proper level of data quality and freshness. Through theoretical and experimental assessments, we demonstrate the capacity of our approach to constantly find a near-optimal tradeoff between sensors and network usage, and measurement quality. In our experimental validation, we have successfully scaled up the battery lifetime of a temperature sensor from a monthly to a yearly basis.

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Section: On the Positive Influence Of Automated Sampling On Power Conmentioning

confidence: 99%

Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

Cecchinel¹,

Fouquet²,

Mosser³

et al. 2019

Future Generation Computer Systems

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“…However, the transmission distance affects the total delay. In [9], [10], [11], authors give the comparison of different MAC techniques used for BAN. Authors perform the comparison between TDMA, CSMA/CA, Pure and Slotted ALOHA, S-ALOHA, and Frequency Division Multiple Access (FDMA).…”

Section: Introductionmentioning

confidence: 99%

An Energy Consumption Analysis of Beacon Enabled Slotted CSMA/CA IEEE 802.15.4

Rasheed

Javaid

Haider

et al. 2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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Due to low power consumption and low duty cycle, IEEE 802.15.4 standard is widely used for Low Rate Wireless Personal Area Networks (LR − W P ANs). It works in Beacon Enabled (BEn) and Non Beacon Enabled (NBEn) mode. In BEn mode, it has Contention Access Period (CAP) and an optional Contention Free Period (CFP). During CAP, nodes contend to access the medium for communication while CFP period provides Guaranteed Time Slot (GTS) for low latency and applications requiring specific bandwidth. In this paper, we evaluate the comprehensive energy consumption of slotted CSMA/CA algorithm of IEEE 802.15.4 standard during idle and backoff periods. Simulation results with acknowledgment frame are given at the end of the paper.

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“…However, as all the devices must be perfectly synchronized, an additional expenditure of energy is necessary in control messages and allocation of time slots, which will be higher for lower latency in communications. TDMA is more suitable for static networks with a limited number of sensors generating data at a fixed rate [13]. In recent years, many TDMA protocols have been proposed in the context of BSNs.…”

Section: Introductionmentioning

confidence: 99%

Low-Power Platform and Communications for the Development of Wireless Body Sensor Networks

Naranjo-Hernández

Roa

Reina‐Tosina

et al. 2015

International Journal of Distributed Sensor Networks

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Although the roles of body sensor networks (BSNs) are similar to those carried out by the generic wireless sensor networks (WSNs), new solutions must be established to optimize communications for true pervasive biomedical monitoring transparent to the user. In this paper, a proposal of a hardware and software platform for biomedical sensors is performed, which is specially designed to minimize energy consumption in BSNs through a modular processing scheme based on the detection of events and information abstraction. The data flow is implemented through a novel communications protocol that enhances the performances of consumption and time delay of the platform. A novel aspect of the protocol is the explicit incorporation of an additional level of communications to support the distributed processing architecture that allows the execution of multiple applications in parallel within the smart sensors. The results obtained with an implementation of a smart sensor for fall detection demonstrate its feasibility as well as the viability of the communication protocol for the development of energy-efficient BSNs.

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Energy Efficient MAC Protocols

Cited by 20 publications

References 13 publications

Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

An Energy Consumption Analysis of Beacon Enabled Slotted CSMA/CA IEEE 802.15.4

Low-Power Platform and Communications for the Development of Wireless Body Sensor Networks

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