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Table 1 Comparison of the state-of-the-art work

From: Sink mobility aware energy-efficient network integrated super heterogeneous protocol for WSNs

Technique Features Domain Flaws/deficiencies Results achieved
LEACH [9] Clustering algorithm Homogeneous WSNs Transmissions and receptions within the allocated TDMA only, cluster heads are elected randomly hence optimal number and distribution of cluster heads cannot be ensured and cannot be used with large-scale WSNs Better network lifetime, high data delivery ratio
HEED [10] Hybrid but fully distributed clustering scheme Multi-hop WSN clustering algorithm More CHs are generated than the expected number and this accounts for unbalanced energy consumption in the network, significant overhead in the network causes noticeable energy dissipation resulting in lower network lifetime Uniform CH distribution across the network and load balancing; multi-hop fashion between the CHs and the BS promote more energy conservation and scalability
SEP [11] Hierarchically clustered scheme Heterogeneous network for WSNs Supports only static nodes, does not support more than two levels of hierarchy in terms of energy Weighted election probability for becoming a CH, longer stability period scalable
DEEC [12] Clustering-based algorithm Heterogeneous aware network for WSNs Overhead and complexity of forming clusters in multiple levels implementing threshold-based functions CH selected on the basis of probability of ratio of residual energy and average energy of the network, a node having more energy has more chances to be a CH. It prolongs the lifetime of the network
DDEEC [13] Energy-aware adaptive clustering based algorithm Heterogeneous network for WSNs As the initial energy of nodes is reduced and as time passed by, advanced nodes will have the same CH selection probability like the normal ones. Permits to balance CH selection on the basis of residual energy, a node having more energy has more chances to be a CH. It prolongs the lifetime of the network.
EDEEC [14] Energy-aware adaptive clustering-based algorithm Heterogeneous network for WSNs 3 types of nodes involved, more level of complexity involved More data packets received at base station, a node having more energy has more chances to be a CH. It prolongs the lifetime of the network and the stability period.
BEENISH [15] Multi-level energy-based scheme Heterogeneous network for WSNs 4 types of nodes involved, more level of complexity involved, ultra-super, super, and advanced nodes are more punished than the normal ones Longer stability periods, enhanced network lifetime increased number of messages sent to the BS
EDR [16] Data routing scheme Data centric routing for WSNs Limited to data centric routing, does not support cluster-based routing, improper load balancing Ability to use in both event-driven and query-driven applications, ensuring shortest, routing path, transmitting very less number of packets, significant power savings
EACLE [17] Tree-rooted distributed clustering scheme Transmission power controlled WSNs Does not support outdoor wireless channel model, only a single sink for hundred of sensors deployed Avoids packet collision, facilitates packet binding, energy-efficient
HRLS [19] Hierarchical location service based scheme Practical distributed location serviced WSNs More energy is consumed in the computation processes Provides sink location information in a scalable and distributed manner, each sink in HRLS distributively constructs its own hierarchy of grid rings
TTDD [23] Geographic routing-based scheme Low power scheme WSNs for efficient data delivery Forwarding path is not the shortest path and may lead to large latency for longer path; grid structure formation and query flooding cost large energy consumption sensor nodes can productively establish a structure to set up forwarding information, effective in high mobility scenarios, better suited to event-detecting WSNs
Technique Features Domain Flaws/deficiencies Results achieved
DLA [27]] Localization-based scheme Spatially constrained WSNs More energy is consumed in the computation processes Position estimation performed by each node in an iterative manner, constraints enable nodes to update their positions on regular intervals; for reducing energy consumption, a stopping criteria for wireless transmissions has been introduced
VGDRA [28] Grid-based dynamic scheme Dynamic route adjustment technique for WSNs Only a few nodes are able to adjust their data routes for data delivery Minimizes the remonstration cost of routes and maintain optimal routes near the mobile sink stop which minimizes the energy consumption of nodes, improves network lifetime