Energy Conservation in Multimedia Big Data Computing … 51 listening phase. For better energy management, the architecture combines LoRaTM and wake up radio. This results in increased communication efficiency and reduced power consumption.
Yang et al. [48] explore resource distribution for a machine to machine-aided cellular network to achieve energy efficiency and nonlinear energy harvesting. The proposed method uses two major access strategies, NOMA (Non-Orthogonal Multi- ple Access) and TDMA (Time-Division Multiple Access). This method attempts to reduce the total energy consumption in the network through joint circuit power con- trol and time allocation. The authors state that both access strategies can be used for optimal machine communication with minimum energy consumption and improved throughput. Energy consumption of each machine type communication device is defined as a convex function with regard to the assigned communication duration.
Using the optimum transmission power conditions of machine type communication devices, the optimization issue for NOMA can be transformed into an equivalent issue whose solution can be derived suboptimally. The paper also discusses the transfor- mation of the original TDMA optimization to an equivalent tractable problem by considering appropriate variable transformation. This transformed problem can then be solved iteratively. The authors show that NOMA requires less amount of energy compared to TDMA with low circuit power control machine type communication devices. In the case of high circuit power control of machine type communica- tion devices, TDMA does better than NOMA, in terms of energy efficiency. The paper also analyses the total energy consumed in NOMA and TDMA policies in uplink M2M communications. Energy minimization problem is stated in terms of circuit power consumption, throughput, energy causality, and transmission power constraints. Either NOMA or TDMA can be used based on the circuit power control in machine type communication devices.
Table 1 Existing energy conservation mechanisms for IoT applications
Protocol Category Key
techniques implemented
Advantages Limitations Approach
[31] Energy-
efficient communica- tion
QoI-aware energy- efficient framework
Transparent and compatible with lower protocols
Applicable in specific scenarios
Uses sensor- to-task relevancy and critical covering set concepts GREENNET
[42]
Green IoT Energy- efficient protocol stack for sensor nodes
Improved performance of existing protocols
Limited network capacity
Uses photovoltaic cell energy- enabled hardware platform
[41] Green IoT Key enablers
and methods to implement green IoT
Integration of IoT domains for smooth interaction
Discusses only theoretical aspects
Various application domains of green IoT
[32] Energy-
efficient communica- tion
Multihop networking, blind cooperative clustering
Reduced overhead in the underlying protocol, improved scalability
Efficiency depends on cluster size
Sets upper bound for mean transmit power level
[33] Energy-
efficient communica- tion
Hybrid FRAM- SRAM MCUs, energy alignment
Platform portability reduced power consumption
Computation complexity
Uses optimal memory maps
DAEECI [44] Energy harvesting
Data awareness, cluster head selection using active RFID tags
Energy saving cluster head selection, improved lifetime
None Computes
energy consumption models in each round
[34] Energy-
efficient communica- tion
Integrated capacity- centric OAN backhaul and a coverage- centric multi-RAT front-end network
Improved battery life
None Uses
converged Fi-Wi access networks
(continued)
Energy Conservation in Multimedia Big Data Computing … 53 Table 1 (continued)
Protocol Category Key
techniques implemented
Advantages Limitations Approach
EcoSense [45]
Energy storage and harvesting
Hardware based on-demand sensing technique
Energy consumption only for desired events
Useful for only short-range applications
Controlled connection between sensors and the power supply unit
[46] Energy
storage and harvesting
Energy- aware routing protocol
Prolonged network lifetime, prevents energy hole
Cannot be used with existing routing protocols, increased computa- tional complexity
Additional information needs to be maintained at every node
EEIoT [35] Energy- efficient communica- tion
Modified MECA algorithm
Self- adaptation of energy harvesting
Specific to big data platforms
Based on MECA
[36] Energy-
efficient routing
Scalable energy efficient clustering
Fair distribution of energy, prolonged network lifetime
None Hybrid
routing protocol for M2M sensor networks
[47] Energy
harvesting
Energy efficient multi- sensing platform
Reduced latency, self- sustainability
None Heterogeneous,
long-range device com- munication
[37] Energy-
efficient secure routing
Energy- aware load balancing routing protocol
Location privacy, fair energy consumption
Increased packet forwarding
Uses path diversity
[48] Energy
harvesting
Circuit power control
Improved throughput, optimal machine communica- tion
Selection of access mechanism is difficult
Considers NOMA and TDMA access mechanisms
(continued)
Table 1 (continued)
Protocol Category Key
techniques implemented
Advantages Limitations Approach
ESMR [38] Energy- efficient routing
Energy- efficient self- organizing multicast routing
Prolonged network lifespan, improved packet success rates
None Uses AVL
tree pruning
MAEER [39] Energy- efficient routing
Energy- efficient mobility- aware routing
Supports mobility, improved packet delivery ratio
High memory requirement
Reduces the number of participating nodes for optimal route discovery EH-mulSEP
[43]
Green IoT Energy harvesting enabled multi-level stable election
Improved scalability, throughput, network lifetime
Uses fixed traffic patterns
Uses multi-level weighted election probability on heteroge- neous nodes Modified
LEACH [40]
Energy- efficient routing
Threshold- based cluster head selection
Enhanced throughput, network lifespan
Cannot be used with heteroge- neous routing
Modification in LEACH protocol
erogeneous working environments, adjustment of duty cycles, access control, and congestion avoidance techniques, sleep time control techniques during inactivity periods, switch off and standby time of radio, resource management and scheduling, efficient cluster head selection schemes, prolonged network lifetime, throughput, scalability, and so on. As features of IoMT and IoT are almost similar, energy con- servation techniques proposed for IoT systems can be used for IoMT applications to achieve energy efficiency. The survey presented in this paper evaluates the existing solutions considering various performance metrics to address energy conservation issues.
Energy Conservation in Multimedia Big Data Computing … 55
References
1. Y. Agarwal, A.K. Dey, Toward building a safe, secure, and easy-to-use internet of things infrastructure. IEEE Comput.49(4), 88–91 (2016)
2. A. Sheth, Internet of things to smart iot through semantic, cognitive, and perceptual computing.
IEEE Intell. Syst.31(2), 108–112 (2016)
3. M. Weyrich, C. Ebert, Reference architectures for the internet of things. IEEE Softw.33(1), 112–116 (2016)
4. S.M. Alzahrani, Sensing for the internet of things and its applications, in2017 IEEE 5th International Conference on Future Internet of Things and Cloud: Workshops (W-FiCloud) (IEEE, 2017), pp. 88–92
5. C. Rostetter, S. Khoshafian, The Adaptive Digital Factory: IoT Reference Architectures (2016), https://www.pega.com/insights/articles/adaptive-digital-factory-iot-reference-architecture 6. S.A. Alvi, B. Afzal, G.A. Shah, L. Atzori, W. Mahmood, Internet of multimedia things: vision
and challenges. Ad Hoc Netw.33, 87–111 (2015)
7. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor.
17(4), 2347–2376 (2015)
8. A. Rodriguez, A. Ordóñez, H. Ordoñez, Energy consumption optimization for sensor net- works in the IoT, in2015 IEEE Colombian Conference on Communications and Computing (COLCOM)(IEEE, 2015), pp. 1–6
9. T. Houret, L. Lizzi, F. Ferrero, C. Danchesi, S. Boudaud, Energy efficient reconfigurable antenna for ultra-low power IoT devices, in2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting(IEEE, 2017), pp. 1153–1154 10. T.D. Nguyen, J.Y. Khan, D.T. Ngo, A distributed energy-harvesting-aware routing algorithm
for heterogeneous IoT networks. IEEE Trans. Green Commun. Netw. (2018)
11. P.V. Krishna, M.S. Obaidat, D. Nagaraju, V. Saritha, CHSEO: an energy optimization approach for communication in the internet of things, inGLOBECOM 2017–2017 IEEE Global Com- munications Conference(IEEE, 2017), pp. 1–6
12. S. Santiago, L. Arockiam, A novel fuzzy based energy efficient routing for internet of things, in2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET)(IEEE, 2017), pp. 1–4
13. Z. Sun, C.H. Liu, C. Bisdikian, J.W. Branch, B. Yang, QoI-aware energy management in internet-of-things sensory environments, in2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)(IEEE, 2012), pp. 19–27
14. S. Mallick, A.Z.S.B. Habib, A.S. Ahmed, S.S. Alam, Performance appraisal of wireless energy harvesting in IoT, in2017 3rd International Conference on Electrical Information and Com- munication Technology (EICT)(IEEE, 2017), pp. 1–6
15. M.E. Khanouche, Y. Amirat, A. Chibani, M. Kerkar, A. Yachir, Energy-centered and QoS-aware services selection for internet of things. IEEE Trans. Autom. Sci. Eng.13(3), 1256–1269 (2016) 16. D. Chen, W. Yang, J. Hu, Y. Cai, X. Tang, Energy-efficient secure transmission design for the
internet of things with an untrusted relay. IEEE Access6, 11862–11870 (2018)
17. Q. Ju, H. Li, Y. Zhang, Power management for kinetic energy harvesting IoT. IEEE Sens. J.
18(10), 4336–4345 (2018)
18. C.X. Mavromoustakis, J.M. Batalla, G. Mastorakis, E. Markakis, E Pallis, Socially oriented edge computing for energy awareness in IoT architectures. IEEE Commun. Mag.56(7), 139–145 (2018)
19. T. Wan, Y. Karimi, M. Stanacevic, E. Salman, Energy efficient AC computing methodology for wirelessly powered IoT devices, in2017 IEEE International Symposium on Circuits and Systems (ISCAS)(IEEE, 2017), pp. 1–4
20. S.S. Prasad, C. Kumar, An energy efficient and reliable internet of things, in2012 International Conference on Communication, Information & Computing Technology (ICCICT)(IEEE, 2012), pp. 1–4
21. C.C. Liao, S.M. Cheng, M. Domb, On designing energy efficient wi-fi P2P connections for internet of things, in2017 IEEE 85th Vehicular Technology Conference (VTC Spring)(IEEE, 2017), pp. 1–5
22. S. Kim, S. Kim, A multi-criteria approach toward discovering killer IoT application in Korea.
Technol. Forecast. Soc. Chang.102, 143–155 (2016)
23. M. Haghighi, K. Maraslis, T. Tryfonas, G. Oikonomou, A. Burrows, P. Woznowski, R.
Piechocki, Game theoretic approach towards optimal multi-tasking and data-distribution in IoT, in2015 IEEE 2nd World Forum on Internet of Things (WF-IoT)(IEEE, 2015), pp. 406–411 24. M. Esmaeili, S. Jamali, A survey: optimization of energy consumption by using the genetic
algorithm in WSN based internet of things. CIIT Int. J. Wirel. Commun. (2016)
25. M.H. Asghar, N. Mohammadzadeh, Design and simulation of energy efficiency in node based on MQTT protocol in internet of things, in2015 International Conference on Green Computing and Internet of Things (ICGCIoT)(IEEE, 2015), pp. 1413–1417
26. A. Musaddiq, Y.B. Zikria, O. Hahm, H. Yu, A.K. Bashir, S.W. Kim, A survey on resource management in IoT operating systems. IEEE Access6, 8459–8482 (2018)
27. P. Ryan, R. Watson, Research challenges for the internet of things: what role can OR play?
Systems5(1), 24 (2017)
28. Z. Abbas, W. Yoon, A survey on energy conserving mechanisms for the internet of things:
wireless networking aspects. Sensors15(10), 24818–24847 (2015)
29. C. Chilipirea, A. Ursache, D.O. Popa, F. Pop, Energy efficiency and robustness for IoT: building a smart home security system, in2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)(IEEE, 2016), pp. 43–48
30. H. Khodr, N. Kouzayha, M. Abdallah, J. Costantine, Z. Dawy, Energy efficient IoT sensor with RF wake-up and addressing capability. IEEE Sens. Lett.1(6), 1–4 (2017)
31. C.H. Liu, J. Fan, J. Branch, K. Leung, Towards QoI and energy-efficiency in internet-of-things sensory environments. IEEE Trans. Emerg. Top. Comput.1, 1, 2014
32. A. Bader, M.S. Alouini, Blind cooperative routing for scalable and energy-efficient internet of things, in2015 IEEE Globecom Workshops (GC Wkshps)(IEEE, 2015), pp. 1–6
33. H. Jayakumar, A. Raha, V. Raghunathan, Energy-aware memory mapping for hybrid FRAM- SRAM MCUs in IoT edge devices, in2016 29th International Conference on VLSI Design and 2016 15th International Conference on Embedded Systems (VLSID)(IEEE, 2016), pp. 264–269 34. D.P. Van, B.P. Rimal, J. Chen, P. Monti, L. Wosinska, M. Maier, Power-saving methods for internet of things over converged fiber-wireless access networks. IEEE Commun. Mag.54(11), 166–175 (2016)
35. K. Suresh, M. RajasekharaBabu, R. Patan, EEIoT: energy efficient mechanism to leverage the internet of things (IoT), inInternational Conference on Emerging Technological Trends (ICETT)(IEEE, 2016), pp. 1–4
36. B.R. Al-Kaseem, H.S. Al-Raweshidy, Scalable M2M routing protocol for energy efficient IoT wireless applications, in2016 8th Computer Science and Electronic Engineering (CEEC) (IEEE, 2016), pp. 30–35
37. A.M.I. Alkuhlani, S.B. Thorat, Enhanced location privacy and energy saving technique for sensors in internet of things domain, in2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)(IEEE, 2016), pp. 122–125
38. S. Nisha, S.P. Balakannan, An energy efficient self organizing multicast routing protocol for internet of things, in2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)(IEEE, 2017), pp. 1–5
39. S.S. Chaudhari, S. Maurya, V.K. Jain, MAEER: mobility aware energy efficient routing protocol for internet of things
40. T. Behera, U.C. Samal, S. Mohapatra, Energy efficient modified LEACH protocol for IoT application. IET Wirel. Sens. Syst. (2018)
41. F.K. Shaikh, S. Zeadally, E. Exposito, Enabling technologies for green internet of things. IEEE Syst. J.11(2), 983–994 (2017)
Energy Conservation in Multimedia Big Data Computing … 57 42. L.O. Varga, G. Romaniello, M. Vuˇcini´c, M. Favre, A. Banciu, R. Guizzetti, C. Planat et al., GreenNet: an energy-harvesting IP-enabled wireless sensor network. IEEE Internet Things J.
2(5), 412–426 (2015)
43. A.S.H. Abdul-Qawy, T. Srinivasulu, EH-mulSEP: energy-harvesting enabled multi-level SEP protocol for IoT-based heterogeneous WSNs, in 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)(IEEE, 2017), pp. 143–151
44. C. Mahapatra, Z. Sheng, V.C. Leung, Energy-efficient and distributed data-aware clustering protocol for the internet-of-things, in2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)(IEEE, 2016), pp. 1–5
45. Y. Liu, Q. Chen, G. Liu, H. Liu, Q. Yang, EcoSense: a hardware approach to on-demand sensing in the internet of things. IEEE Commun. Mag.54(12), 37–43 (2016)
46. L. Rosyidi, R.F. Sari, Energy harvesting aware protocol for 802.11-based internet of things network, in2016 IEEE Region 10 Conference (TENCON)(IEEE, 2016), pp. 1325–1328 47. M. Magno, F.A. Aoudia, M. Gautier, O. Berder, L. Benini, WULoRa: an energy efficient
IoT end-node for energy harvesting and heterogeneous communication, inProceedings of the Conference on Design, Automation & Test in Europe(European Design and Automation Association, 2017), pp. 1532–1537
48. Z. Yang, W. Xu, Y. Pan, C. Pan, M. Chen, Energy efficient resource allocation in machine-to- machine communications with multiple access and energy harvesting for IoT. IEEE Internet Things J. 1–1, 2017