Increasing Bluetooth Low Energy communication efficiency by presetting protocol parameters

Increasing Bluetooth Low Energy communication efficiency by presetting protocol parameters

Standard protocols are important regarding the compatibility of devices provided by different vendors.However, specific applications have various requirements and do not always need all features offered by standardprotocols, making them inefficient. This paper focuses on standard Bluetooth Low Energy modifications, reducingcontrol overhead for the intended healthcare application. Specifically, the connection establishment, device pairing,and connection parameter negotiations have been targeted. The simulation-based experiments showed over 20 timesreduction of control-overhead time preceding a data transmission. It does not just directly increase the energy efficiencyof communication; it also prolongs the time for sensor-based end devices to spend in an energy-saving mode. The resultis a longer runtime of such sensor devices powered by batteries.

___

  • [1] Bluetooth Special Interest Group. Specification of the Bluetooth System. Covered Core Package version: 4.2. Bluetooth SIG, 2014.
  • [2] Soua R, Minet P. A survey on energy efficient techniques in wireless sensor networks. In: 2011 4th Joint IFIP Wireless and Mobile Networking Conference; Toulouse, France; 2011. pp. 1-9.
  • [3] Alippi C, Anastasi G, Di Francesco M, Roveri M. Energy management in wireless sensor networks with energy-hungry sensors. IEEE Instrumentation & Measurement Magazine 2009; 12 (2): 16-23. doi: 10.1109/MIM.2009.4811133
  • [4] Kolios P, Ellinas G, Panayiotou C, Polycarpou M. Energy efficient event-based networking for the Internet of Things. In: 2016 IEEE 3rd World Forum on Internet of Things; Reston, VA, USA; 2016. pp. 1-6.
  • [5] Feng H, Ma L, Leng S. A low overhead wireless sensor networks MAC protocol. In: 2010 2nd International Conference on Computer Engineering and Technology; Chengdu, China; 2010. pp. V4-128-V4-131.
  • [6] Ogundile OO, Alfa AS. A survey on an energy-efficient and energy-balanced routing protocol for wireless sensor networks. Sensors 2017; 17 (5): 1084. doi: 10.3390/s17051084
  • [7] Fafoutis X, Tsimbalo E, Piechocki R. Timing channels in Bluetooth Low Energy. IEEE Communications Letters 2016; 20 (8): 1587-1590. doi: 10.1109/LCOMM.2016.2574311
  • [8] Carrano RC, Passos D, Magalhaes LCS, Albuquerque CVN. Survey and taxonomy of duty cycling mechanisms in wireless sensor networks. IEEE Communications Surveys Tutorials 2014; 16 (1): 181-194. doi: 10.1109/SURV.2013.052213.00116
  • [9] Chmaj G, Selvaraj H. Energy-efficient distributed computing solutions for Internet of Things with ZigBee devices. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science; Las Vegas, NV, USA; 2015. pp. 437-442.
  • [10] Mikhaylov K, Hänninen T. Mechanisms for improving throughput and energy efficiency of Bluetooth Low Energy for multi node environment. Journal of High Speed Networks 2015; 21 (3): 165-180. doi: 10.3233/JHS-150518
  • [11] Bluetooth Special Interest Group. Bluetooth Core Specification v 5.0. Bluetooth SIG, 2016.
  • [12] Collotta M, Pau G, Talty T, Tonguz OK. Bluetooth 5: A concrete step forward toward the IoT. IEEE Communications Magazine 2018; 56: 125-131. doi: 10.1109/MCOM.2018.1700053
  • [13] Gomez C, Oller J, Paradells J. Overview and evaluation of Bluetooth Low Energy: an emerging low-power wireless technology. Sensors 2012; 12 (9): 11734-11753. doi: 10.3390/s120911734
  • [14] Cho K, Park W, Hong M, Park G, Cho W et al. Analysis of latency performance of Bluetooth low energy (BLE) networks. Sensors 2015; 15 (1): 59-78. doi: 10.3390/s150100059
  • [15] Gupta S, Dham R. Improving security with Bluetooth Low Energy 4.2. Embedded Systems Engineering: IoT Security 2016.
  • [16] Dingle NJ, Knottenbelt WJ, Suto T. PIPE2: a tool for the performance evaluation of generalised stochastic Petri Nets. ACM SIGMETRICS Performance Evaluation Review 2009; 36 (4): 34-39. doi: 10.1145/1530873.1530881
  • [17] Fafoutis X, Vafeas A, Janko B, Sherratt RS, Pope J et al. Designing wearable sensing platforms for healthcare in a residential environment. EAI Endorsed Transactions on Pervasive Health and Technology 2017; 3 (12): 1-11. doi: 10.4108/eai.7-9-2017.153063
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
Sayıdaki Diğer Makaleler

Design of a substrate integrated waveguide matrix amplifier

Gholamreza MORADI, Shabnam AHMADI ANDEVARI

Lower order controller design using weighted singular perturbation approximation

Muhammad Raees Furquan AZHAR, Muwahida LIAQUAT, Umair ZULFIQAR

Exploiting stochastic Petri nets with fuzzy parameters to predict efficient drug combinations for Spinal Muscular Atrophy

Rza BASHIROV, Nimet AKÇAY, Adil ŞEYTANOĞLU, Mani MEHRAEI, Recep DURANAY

I see EK: A lightweight technique to reveal exploit kit family by overall URL patterns of infection chains

Nazife BAYKAL, Emre SÜREN, Pelin ANGIN

A no-reference framework for evaluating video quality streamed through wireless network

Muhammad Nasir KHAN, Muhammad UZAIR, Bilal A. KHAWAJA, Robert D. DONY, Mohsin JAMIL

A novel hardware-efficient spatial orientation tree-based image compression algorithm and its field programmable gate array implementation

Mohd Rafi LONE, Najeeb-ud-Din HAKIM

Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs

Sahar SADEGHI, Ali MALEKI

A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map

Jyun-Yu JHANG, Kuu-Young YOUNG, Chin-Teng LIN, Cheng-Jian LIN

SoftSwitch: a centralized honeypot-based security approach using software-defined switching for secure management of VLAN networks

Resul DAŞ, Muhammet BAYKARA

A robust SMES control for enhancing stability of distribution systems fed from intermittent wind power generation

Sayed SAID, Bálint HARTMANN, Mokhtar ALY