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EDDEEC: Enhanced Developed Distributed Energy-Efficient Clustering for Heterogeneous Wireless Sensor Networks
N. Javaid, T. N. Qureshi, A. H. Khan, A. Iqbal, E. Akhtar, M. Ishfaq
TL;DR
WSNs face high energy demands and limited, non-replaceable batteries, making energy-efficient routing and longer network operation important. EDDEEC addresses this with dynamic, residual-energy-aware cluster-head probabilities for three-level heterogeneous networks, and simulations report better stability, lifetime, and base-station delivery than DEEC, DDEEC, and EDEEC. The reported results support EDDEEC as an energy-aware clustering protocol under the tested settings.
Problem
WSNs require energy-intensive sensing and transmission, while deployed nodes have limited batteries that generally cannot be replaced or recharged.
Method
EDDEEC dynamically adjusts cluster-head election probabilities using node energy and network average energy, with equal probabilities below Tabsolute in three-level heterogeneous networks.
Results
First-node death occurred at 1717 rounds for EDDEEC versus 969 for DEEC, 1355 for DDEEC, and 1432 for EDEEC; all nodes died at 8638 rounds for EDDEEC and EDEEC.
Takeaways & Limitations
EDDEEC achieved the strongest reported performance across stability period, network lifetime, and packets successfully sent to the base station among the compared protocols.
Takeaways & Limitations
The simulations used fixed or micromobile nodes, a centered base station, and ignored signal-collision, interference, and dynamic-channel energy losses.
Abstract
from arXiv · showhide
Wireless Sensor Networks (WSNs) consist of large number of randomly deployed energy constrained sensor nodes. Sensor nodes have ability to sense and send sensed data to Base Station (BS). Sensing as well as transmitting data towards BS require high energy. In WSNs, saving energy and extending network lifetime are great challenges. Clustering is a key technique used to optimize energy consumption in WSNs. In this paper, we propose a novel clustering based routing technique: Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous WSNs. Our technique is based on changing dynamically and with more efficiency the Cluster Head (CH) election probability. Simulation results show that our proposed protocol achieves longer lifetime, stability period and more effective messages to BS than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) in heterogeneous environments.
1. Background
WSNs use clustering to reduce energy consumption when battery-constrained nodes send sensed data to a base station. Heterogeneous protocols account for nodes having different initial energy levels.
- Battery-constrained sensor nodes cannot generally be recharged or replaced after deployment.
- Clustering assigns nodes to groups whose members send data to a cluster head for aggregation before transmission to the base station.
- Homogeneous WSNs contain nodes with the same energy level, whereas heterogeneous WSNs deploy nodes with different initial energy.
- LEACH, PEGASIS, and HEED are homogeneous-network examples, while SEP, DEEC, DDEEC, and EDEEC target heterogeneous WSNs.
2. EDDEEC Protocol
EDDEEC extends energy-aware cluster-head selection to three-level heterogeneous WSNs by dynamically adapting node probabilities according to residual energy. Its key adjustment equalizes selection probabilities below an absolute residual-energy threshold, with simulations selecting z = 0.7.
- EDDEEC Protocol: EDDEEC bases cluster-head election probabilities on initial energy, residual energy, and the network’s average energy, following DEEC’s general approach.
- EDDEEC Protocol: The protocol estimates network rounds, per-round energy dissipation, distances, and an optimal cluster count to support energy-aware operation.
- EDDEEC Protocol: At each round, eligible nodes decide whether to become cluster heads using a threshold based on their selection probability.
- EDDEEC Protocol: The protocol models normal, advanced, and super nodes to represent three levels of heterogeneity.
- EDDEEC Protocol: EDDEEC modifies EDEEC probabilities because repeated cluster-head selection can reduce advanced and super nodes to normal-node residual energy levels.
- EDDEEC Protocol: Below Tabsolute, normal, advanced, and super nodes receive the same cluster-head selection probability.
- EDDEEC Protocol: Tabsolute is parameterized as zE0, where z ∈ (0, 1); simulations varying z found the best first-dead-node result at z = 0.7.
3. Simulations and Results
The simulations evaluate heterogeneous WSN clustering protocols using network lifetime, stability period, and packets sent to the Base Station. EDDEEC is compared with DEEC, DDEEC, and EDEEC under specified network assumptions and node distributions.
- Simulation setup: The simulations use 100 randomly placed nodes in a 100m × 100m field, with fixed or micromobile nodes and the Base Station at the field center.Energy loss from signal collisions and interference is ignored.
- Evaluation metrics: Performance is evaluated by stability period, heterogeneous-network lifetime, and data packets successfully sent to the Base Station.
- Node configuration: The three-level heterogeneous network contains 20 normal nodes, 32 advanced nodes with 2.0 times greater energy, and 48 super nodes with 3.5 times greater energy.
- Results: The first node dies at 969, 1355, 1432, and 1717 rounds for DEEC, DDEEC, EDEEC, and EDDEEC, respectively.
- Results: All nodes die at 5536, 5673, 8638, and 8638 rounds for DEEC, DDEEC, EDEEC, and EDDEEC, respectively.
- Results: EDDEEC sends more data packets to the Base Station than the other selected protocols.
4. Conclusion
The conclusion presents EDDEEC as an adaptive, energy-aware protocol for heterogeneous WSNs. Simulations using stability period, network lifetime, and packets sent to the Base Station report better results than DEEC, DDEEC, and EDEEC.
- 4. Conclusion: EDDEEC dynamically changes node probabilities for becoming Cluster Heads to distribute energy among sensor nodes in a balanced and efficient way.
- 4. Conclusion: The authors assess EDDEEC using stability period, network lifetime, and packets sent to the Base Station.
- 4. Conclusion: Simulation analysis reports EDDEEC as more efficient and reliable than DEEC, DDEEC, and EDEEC.