Performance enhancement routing protocols for heterogeneous WSNs

Mohamed, Fathia A.; Hassan, Emad S.; Dessouky, Moawad I.; Elsafrawey, Amir S.

doi:10.1002/dac.4223

Cited by 7 publications

(7 citation statements)

References 14 publications

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Section: Circle Zone Stable Election Protocolmentioning

confidence: 99%

“…We calculated E tot.adv , E tot.helper , respectively using (4) and (15). Similarly, let E tot.int be the total initial energy for all intermediate nodes, each intermediate node contains b time more energy than the normal node.…”

Section: Modified Czsep With Helper Nodes (Mczsep-hn)mentioning

confidence: 99%

“…To solve the far nodes problem in ZSEP we proposed an EZSEP [15]. This protocol is trying to resolve the main problem in ZSEP by dividing the network field into five zones ( N ‐zone 0, A ‐zone 1, A ‐zone 2, A ‐zone 3 and A ‐zone 4) to improve the communication of remoting nodes from the BS.…”

Section: Related Workmentioning

confidence: 99%

“…This distribution reduces the number of normal nodes that are far from the sink (BS). Therefore, the proposed protocol improves the stability period of the normal nodes and enhances the network life‐time compared to conventional ZSEP and EZSEP [14, 15]. In CZSEP, the network field is divided into two zones: N ‐Zone 0 and A ‐Zone1 illustrated as follows: N‐Zone 0 (N is a shortcut for normal nodes): this is a circle zone at the centre of the network field, where the normal nodes are deployed in this zone and use the DT technique to communicate with BS. A‐Zone 1 (A is a shortcut for advanced nodes): this is the second zone surrounding the circle zone, where the advanced nodes are deployed in this zone and use the transmission through CHs technique. Let ( x i , y i ) be the coordinates of the i th advanced node and the coordinates of CH are ( x CH , y CH ) located out the circle zone.…”

Section: Proposed Protocolsmentioning

confidence: 99%

“…In heterogeneous routing protocols, there are more energy levels, diverse types of nodes, diverse optimal probabilities for CHs selecting [11]. These types of protocols include the proactive routing protocols [7], where the nodes in theses protocol sense continuously and give a report of data to the BS such as stable election protocol (SEP) [12], enhanced‐SEP (ESEP) [13], zonal‐SEP (ZSEP) [14] and enhanced‐ZSEP (EZSEP) [15]. As explained above, according to the method by which the data are sent to the BS, there are different transmission techniques in heterogeneous WSNs: DT techniques and the clustering techniques [16, 17].…”

Section: Introductionmentioning

confidence: 99%

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Energy‐efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs

Mohamed

Hassan

Elsafrawey

et al. 2019

IET wirel. sens. syst.

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The design of an energy-efficient routing protocol is crucial for extending the lifetime of limited energy-constrained wireless sensor networks (WSNs). To achieve this goal, the authors propose three proactive routing protocols; the first protocol is a circle zone stable election protocol (CZSEP). This protocol is a two-level heterogeneous energy protocol with a circle zone located at the centre of the network field and used to deploy the normal nodes and with advanced nodes that are randomly distributed around the circle zone. The performance of the proposed CZSEP was compared to that of the conventional ZSEP considering the same total initial energy. To further improve the performance of CZSEP, they propose the second protocol named as, CZSEP with helper nodes (CZSEP-HN) which is a three-level heterogeneous energy protocol. In CZSEP-HN, they add helper nodes in certain places in the network field to minimise the energy consumption in the considered network. Both CZSEP and CZSEP-HN can overcome the problem of large transmission distance between normal nodes and the base station (BS) and between cluster-heads and BS exists in conventional ZSEP. Finally, they propose the third protocol, which is a modified CZSEP-HN (MCZSEP-HN) to overcome the problem of energy consumption in CZSEP-HN. MCZSEP-HN is a fourlevel heterogeneous energy protocol with two circle zones one for normal nodes and the other for intermediate nodes. The obtained results showed that the MCZSEP-HN outperforms the other protocols in terms of lifetime , throughput, stability and instability period and can reduce the energy consumption in WSNs.

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Section: Circle Zone Stable Election Protocolmentioning

confidence: 99%

Section: Modified Czsep With Helper Nodes (Mczsep-hn)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Proposed Protocolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy‐efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs

Mohamed

Hassan

Elsafrawey

et al. 2019

IET wirel. sens. syst.

Self Cite

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Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

Pattnaik

Sahu

2021

Int J Communication

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Summary The selection of good rendezvous points (RPs) is a significant role for M2obile Sink (MS) in the wireless sensor network (WSN). For the mobile sink, the selection of RP is one of the major problems in WSN. The rendezvous points are only selected based on the local information, so the possibility of selecting an optimal sensor node as RP will be extremely low. The next problem is to find the mobile sink path which visits all the RPs. The above problem is comprehended by utilizing an optimization algorithm. In this paper, Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization based clustering approach and hybrid Moth‐flame cuttlefish optimization (MFO‐CFO) algorithm for efficient routing in WSN is proposed. Initially, the number of clusters is formed due to the Fuzzy C‐means (FCM) based Ant lion optimization (ALO) clustering approach. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) based Particle swarm optimization (PSO) technique is presented for best cluster head (CH) selection. Here, residual energy (RE), node degree, and histories are considered as an input parameter to find the optimal cluster head. At last, we use hybrid MFO‐CFO techniques to find the minimum RPs which decreases energy consumption. The performance metrics of end to end (E2E) delay, energy consumption, channel load, throughput, bit error rate (BER), Network Life Time (NLT), packet delivery ratio (PDR), latency, jitter, and packet loss are computed using several nodes. The performance is compared with some existing algorithms such as Genetic Approach (GA), Fuzzy, PSO (Particle Swarm Optimization), Multiple Access Data Gathering using Mobile Sink for Path Constrained Environment (MADGAMSPCE).

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An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

Xiao

Gan

Wang

et al. 2022

Int J Communication

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Summary In recent trends, wireless sensor network (WSN) is often used in harsh environments. When WSNs work, they need to deploy a large number of sensor nodes in the monitoring area. Due to the limited resources carried by the sensor nodes, the monitoring process of WSN will be limited by the energy of the sensor nodes. An energy‐efficient clustering algorithm combining interval type‐2 fuzzy logic and dual‐super‐cluster‐head (IT2FL‐DSCH) mechanism is proposed, aiming at reducing and balancing the energy consumption of sensor nodes. Considering the four fuzzy parameters, that is, cluster heads' (CHs') battery power, CHs' centrality, base station's (BS's) mobility, and the distance from CHs to BS, interval type‐2 fuzzy logic (IT2FL) is used to select the appropriate super cluster head (SCH). And SCH is the only way to communicate with the mobile BS, thus effectively using bandwidth. Then, the DSCH mechanism to balance the energy consumption of SCH is introduced in detail. In addition, we utilize mathematical derivation to obtain the supremum of the energy consumption of the SCH algorithm under different network distribution. Finally, the MATLAB simulation results show that this protocol outperforms low energy adaptive clustering hierarchy (LEACH), LEACH‐centralized (LEACH‐C), distributed energy efficient clustering (DEEC), WSNs using deep‐Q‐networks (DQN), and two‐tier distributed fuzzy logic based protocol (TTDFP) in terms of sensor nodes energy saving, network stability, and network lifetime.

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Performance enhancement routing protocols for heterogeneous WSNs

Cited by 7 publications

References 14 publications

Energy‐efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs

Energy‐efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

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