With the advent of IoT, cloud/fog based healthcare systems have become a growing trend in modern healthcare systems. These systems comprise of smart sensors, which on integration with medical devices, generate heterogeneous medical big data that can be used in diagnosis of various diseases. However, there is a continuous flow of large quantity of data in such a systems, due to which it may face many difficulties. Among various pre-requisites for proper functioning of these systems, lifetime is a vital factor. Keeping in view these aspects, the use of new hybrid whale-PSO algorithm (HWPSO) in clustering has been proposed for prolonging the network lifetime by preserving the power of network edge devices. In addition to this, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been taken into account in the selection of cluster leader. The cluster leader is responsible for managing intra-cluster and inter-cluster data communication.
T.K.Hui, R.S.Sherratt and D.D.Sánchez, Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies, Future Generation Computer Systems76 (2017), 358–369.
2.
P.P.Ray, Internet of things for smart agriculture: Technologies, practices and future direction, Journal of Ambient Intelligence and Smart Environments9(4) (2017), 395–420.
3.
P.Verma and S.K.Sood, Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes, IEEE Internet of Things Journal5(3) (2018), 1789–1796.
4.
A.Majumdar, T.Debnath, S.K.Sood and K.L.Baishnab, Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment, Journal of Medical Systems42(10) (2018), 187.
5.
W.He, G.Yan and L.D.Xu, Developing vehicular data cloud services in the IoT environment, IEEE Transactions on Industrial Informatics10(2) (2014), 1587–1595.
6.
G.Zhou, Z.Liu, W.Shu, T.Bao, L.Mao and D.Wu, Smart savings on private car pooling based on internet of vehicles. Journal of Intelligent& Fuzzy Systems32(5) (2017), 3785–3796.
7.
T.Anagnostopoulos, A.Zaslavsky, K.Kolomvatsos, A.Medvedev, P.Amirian, J.Morley and S.Hadjieftymiades, Challenges and opportunities of waste management in IoT-enabled smart cities: A survey, IEEE Transactions on Sustainable Computing2(3) (2017), 275–289.
8.
F.Shrouf and G.Miragliotta, Energy management based on Internet of Things: Practices and framework for adoption in production management, Journal of Cleaner Production100 (2015), 235–246.
9.
N.M.Laskar, K.Guha, I.Chattarjee, S.Chanda, K.L.Baishnab and P.K.Paul, HWPSO: A new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems, Applied Intelligence49 (2018), 265–291.
10.
J.Jin, J.Gubbi, S.Marusic and M.Palaniswami, An information framework for creating a smart city through internet of things, IEEE Internet of Things Journal1(2) (2014), 112–121.
11.
Z.Zhou, J.Tang, L.J.Zhang, K.Ning and Q.Wang, EGF-tree: An energy-efficient index tree for facilitating multi-region query aggregation in the internet of things, Personal and Ubiquitous Computing18(4) (2014), 951–966.
12.
J.Tang, Z.Zhou, J.Niu and Q.Wang, An Energy Efficient Hierarchical Clustering Index Tree for Facilitating Time-Correlated Region Queries in the Internet of Things, Journal of Network and Computer Applications40 (2014), 1–11.
13.
N.Kaur and S.K.Sood, An Energy-Efficient Architecture for the Internet of Things (IoT), IEEE Systems Journal11(2) (2017), 796–805.
14.
J.M.Liang, J.J.Chen, H.H.Cheng and Y.C.Tseng, An energy-efficient sleep scheduling with QoS consideration in 3GPP LTE-advanced networks for internet of things, IEEE Journal on Emerging and Selected Topics in Circuits and Systems3(1) (2013), 13–22.
15.
J.Y.Chang, A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems, Wireless Personal Communications82 (2015), 757–76.
16.
S.Rani, R.Talwar, J.Malhotra, S.Ahmed, M.Sarkar and H.Song, A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks, Sensors15(11) (2015), 28603–28626.
17.
M.Aazam, E.N.Huh, M.St-Hilaire, C.H.Lung and I.Lambadaris, Cloud of Things: Integration of IoT with Cloud Computing, Robots and Sensor Clouds36 (2015), 77–94.
18.
A.Bagula, A.P.Abidoye and G.A.L.Zodi, Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things, Sensors16(1) (2015), 9.
19.
A.P.Abidoye and I.C.Obagbuwa, Models for Integrating Wireless Sensor Networks into the Internet of Things, IET Wireless Sensor Systems7(3) (2017), 65–72.
20.
Ö.U.Akgül and B.Canberk, Self-Organized Things (SoT): An energy efficient next generation network management, Computer Communications74 (2016), 52–62.
21.
A.Orsino, A.Giuseppe, M.Leonardo, A.Z.Jesus, M.Antonella and I.Antonio, Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications, Sensors16 (2016), 1–19.
22.
P.Kumar, M.Reddy and M.R.Babu, Energy Efficient Cluster Head Selection for Internet of Things, New Review of Information Networking22(1) (2017), 54–70.
23.
L.Song, K.K.Chai, Y.Chen, J.Schormans, J.Loo and A.Vinel, QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems, IEEE Systems Journal11(3) (2017), 1447–1455.
24.
N.T.Van, T.T.Huynh and B.An, An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks, Journal of Intelligent& Fuzzy Systems, (Preprint), 1–8.
25.
T.L.Saaty, The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making, In Multiple criteria decision analysis: State of the art surveys, Springer, New York, NY, 78, 2005, 345–405.
26.
R.K.Roy, Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement. Press: John Wiley and Sons, Inc., 2001.
27.
A.K.Singh, P.K.Patowari and N.V.Deshpande, Experimental analysis of reverse micro-EDM for machining microtool, Materials and Manufacturing Processes31(4) (2016), 530–540.
