Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks
<p>Classification of energy reducing schemes in WSNs.</p> "> Figure 2
<p>Military architecture for monitoring application using the single-hop clustering model in WSNs.</p> "> Figure 3
<p>Precision agriculture architecture for monitoring application using the multi-hop clustering model in WSNs.</p> "> Figure 4
<p>Illustration of the LEDCHE-WSN architecture.</p> "> Figure 5
<p>The proposed Optimum Cluster Head Election method’s data flow diagram.</p> "> Figure 6
<p>The proposed Dynamic Cluster Self-Organization Method’s data flow diagram.</p> "> Figure 7
<p>Total energy consumption for each node considering a 100 × 100 m network size for LEACH, FBCFP, and LEDCHE-WSN protocols.</p> "> Figure 8
<p>Mean package delay considering 100 × 100 m network size for LEACH, FBCFP, and LEDCHE-WSN protocols.</p> "> Figure 9
<p>Percentage of packets loss ratio for the 100 × 100 m network size for LEACH, FBCFP, and LEDCHE-WSN protocols.</p> ">
Abstract
:1. Introduction
2. Concepts of Clustering Approaches
2.1. Cluster Head and Clustering Operations
2.2. Formation of a Cluster
2.3. Homogeneous and Heterogeneous Sensor Networks
2.4. Single-Hop vs. Multi-Hop Models
3. Related Work
4. Proposed Network Model
4.1. Nodes Placement
4.2. Network Model Assumptions
- The network has N sensors distributed in the Q × Q square field;
- Each node has a unique ID;
- The sink node (SN) location is known by all the sensors, and vice versa;
- The network nodes are stationary;
- All the network nodes are homogeneous and energy-constrained;
- The SN of each cluster is located at the center of the cluster, so all nodes of the cluster can be connected with it using the single-hop model method;
- The SN has unlimited energy resource;
- The BSs are located at the center of each network. The BSs collect their data from rechargeable SNs;
- One BS is used for each 100 × 100 m network size;
- Regular sensors can communicate directly with their cluster head (CH);
- The size of a cluster in a network concerning the number of regular sensors is equal;
- In the beginning, all sensor nodes have the same energy level (maximum) and each of them can become the CH or a regular node;
- All the nodes have the capability with appropriate distance to send the information to the sink node.
4.3. Network Architecture Designing
Proposed Lightness Architecture and Protocol Efficiency
5. Proposed Dynamic Cluster Head Election Algorithm
5.1. Proposed Optimum Cluster Head Election Method
Benefits of Random and Periodic Methods Combination
- Strengthening the security side by electing the CH candidate randomly at the beginning of each round, which complicates the attackers’ tasks and attempts as tracking CHs to obtain information;
- Distributing energy among the network nodes fairly, using a periodical election method during each round extending the network lifetime;
- The simplicity, ease, and lightness of the proposed model avoids the complexity burden due to more mathematical operations and messaging for accuracy, which drains the network energy that is supposed to be provided.
Algorithm 1 Working of the proposed method in a single cluster |
Input: ClusterNumber, DevicePower, DeviceId, SIZEOFCLUSTERS Output: Assigning the optimum Cluster Head fn_AssignClusterHead (ClusterNumber, DevicePower) //Function assign Cluster Heads randomly at the beginning of each cycle and periodically with the //rest BEGIN R = Generate a random number from [SIZEOFCLUSTERS] FOR each sensor i elected randomly R at the beginning of each cycle DO DeviceId = ClusterElements [ClusterNumber][i] ClusterHeadPower = DevicePower [DeviceId-1] ->dRemainingPower IF the candidate CH is a live THEN ClusterHeadID = DeviceId Else CALL fn_DeleteDeadSensors (ClusterNumber, DevicePower) END IF RETURN ClusterHeadID END FOR END |
5.2. Proposed Dynamic Re-Clustering and Self-Organization Method
5.2.1. Mitigation of Energy Consumption during Cluster Reconfiguration
5.2.2. Dead Nodes and Re-Clustering Organization
Algorithm 2 Deletes the Dead Sensors from the Cluster |
Input: ClusterNumber, DevicePower, SIZEOFCLUSTERS Output: Delete the Dead Sensors from the Cluster fn_DeleteDeadSensors (ClusterNumber, DevicePower) // Function delete the Dead Sensors from the Cluster and sends a report to the Base Station (BS) BEGIN FOR each sensor i starting from the dead sensor DO Delete the Dead Sensors by Copy next element value to current element ClusterElements [ClusterNumber][i] = ClusterElements [ClusterNumber] [i + 1] END FOR Decrement SIZEOFCLUSTERS by 1 ClusterElements [ClusterNumber] [SIZEOFCLUSTERS--] Send a report telling the Base Station that the DeviceId (in the ClusterNumber) is dead END |
5.2.3. Multiple Cluster Organization
Algorithm 3 Multiple Cluster Algorithm |
Input: DevicePower, NUMBEROFCLUSTERS Output: Moving between the Clusters to elect the optimum CH for each FOR each Cluster i of the network DO IF (CHcount[i] == NUMBEROFCLUSTERS) THEN CH [i] = CALL f_AssignClusterHead (i, DevicePower) RETURNING elected CH prevCH[i] = CH[i] CHcount[i] = 0 ELSE CHcount[i]++ IF (prevCH [i]! = 0) CH[i] = prevCH[i] END IF END FOR |
5.2.4. Data Collection Phase
Algorithm 4 Identify the cluster of each sensor |
Input: DeviceId, ClusterElements Output: Identify the cluster of each sensor fn_IdentifyCluster (DeviceId) BEGIN For each Cluster i of the network DO For each sensor j of the Cluster DO IF (DeviceId == ClusterElements[i][j]) THEN RETURN i END FOR END FOR END |
Algorithm 5 Data Transmission Phase |
Input: ClusterNumber, DevicePower, DeviceId Output: Transmit all Clusters data to the Sink Node IF the sensor is the Cluster Head THEN forwards the packets to the Sink Node Else Identify the Cluster and the CH of the current Cluster will be the nextHop ClusterId = fn_IdentifyCluster(DeviceId) nextHop = CH[ClusterId] END IF |
6. Performance Comparison and Results Analysis
6.1. Simulation Setup
6.2. Results Analysis
6.2.1. Energy Consumption and Network Lifetime
6.2.2. Mean Package Delay
6.2.3. Total Packets Dropped (Loss Ratio)
6.3. Proposed Protocol Strength Points Compared with LEACH and FBCFP Protocols
- The LEDCHE-WSN protocol adopted a hybrid clustering model. A single-hop model is used at the cluster core, where the nodes are a small size and have a short distance between one another, making it more simple, fast, lightweight, and easy to deploy. On the other hand, a multi-hop model is used at the cluster edge to make it more connective and scalable. LEACH and FBCFP protocols use the multi-hop model at the cluster core. Despite the fact a multi-hop model can improve connectivity and extend network coverage, it causes more packet delay and packet loss, especially at the cluster core. As is well-known, the direct routing approach (single-hop model) is easy to deploy and provides fast delivery, but its drawback appears when the network size grows. Therefore, this problem was overcome by improving the LEDCHE-WSN architecture through distributing sink nodes, and using the multi-hop model at the edge of the cluster to make it much more connective and scalable;
- The LEACH protocol elects the cluster-head randomly, which results in an unbalanced energy level and thus dissipates the total network energy. The proposed algorithm performs a periodical election by excluding failed or dead nodes, which leads to a balanced energy level;
- The proposed algorithm works randomly only at the beginning of each round, to enhance the privacy of location, minimize hackers’ chances of successive packets, and further complicate guessing the next packet;
- The random method in the LEACH protocol caused much more delay, due to the false election of the failure nodes (black holes) and much more time needed to get out of those holes. On the other hand, the proposed protocol ensures the living of the node by checking the remaining power during the random election for the CH candidate to overcome the black holes problem, and that happened only at the beginning of each round;
- The LEACH protocol calculates the remaining energy and times of being elected as a cluster head for each sensor node adds more burden to the network. Moreover, the FBCFP protocol is more complicated, focusing on the degree of accuracy to choose the cluster head performing complex mathematical operations and measuring multiple factors (such as node density, CH degree count, traffic level, and bandwidth availability), causing more delays and increasing complexity. The LEDCHE-WSN algorithm checks the remaining energy just to ensure that a CH candidate is alive, without adding a burden in calculation and comparison for the remaining energy of the CH candidate with all the other sensor nodes as detailed in Section 6.2. (Results Analysis);
- More importantly, the LEDCHE-WSN protocol relieved regular sensors from the processes of selecting the next CH node and notifying the rest of the cluster nodes accordingly. Instead, it assigned all previous jobs to the rechargeable SN nodes, contrary to what happened in most of the previous studies—including the FBCFP protocol. In this protocol, cluster nodes participate in selecting the next CH node and, then, notifies the rest of the cluster nodes. It keeps all the information of the topology list, leading to a significant and effective drain on cluster nodes’ energy;
- The proposed method is distinguished from the LEACH and FBCFP protocols by addressing the problem of re-electing a failed or dead node, checking the status of the node, and then deleting the failed and/or dead nodes from the network topology list, resulting in extending the overall network lifetime.
7. Conclusions and Future Works
Author Contributions
Funding
Conflicts of Interest
References
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Protocol | Year | Technique | Classification | Strengths | Weaknesses |
---|---|---|---|---|---|
Proposed protocol (LEDCHE-WSN) | 2021 | Combined CH election Method Self-configuring cluster formation | Hybrid clustering scheme | (1) Introduces a new and efficient method to elect the CH; (2) Simple, easy, and light; (3) Deletes the dead nodes from the topology list; (4) Self-configuring the re-clustering process dynamically; (5) Complexes tracking the CHs by attackers | Ignore to face the challenge of electing CH node dead/failure while performing its work (Future work) |
FBCFP protocol [18] | 2019 | Neuro-fuzzy rule | Adaptive schemes | It uses a deep learning-based method to ensure electing the optimum CH | Extensive calculation in the cluster head election process drains the cluster nodes’ energy quickly and adds more burdens to the network. |
Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks [24] | 2019 | Derivative-free Nelder–Mead Simplex method | Hybrid clustering scheme | The proposed protocol decreases energy consumption effectively | The heavy calculations of the transmission range of the control messages and the average number of clusters in each round result in a great delay of operations, and the depletion of the network’s energy |
DFLCHES Scheme [25] | 2018 | Genetic algorithms /Elbow method/Fuzzy predictive method | Adaptive schemes | (1) Effective cluster head selection that enhancement the lifetime of the network; (2) Out-performed the KBPSO and LEACH schemes in terms of packet drop ratio, total energy consumption, and number of average delays. | The extensive calculation, such as node centrality measure, hop count, and density, resulting in quick energy depletion, consequently shortening the lifetime of the network |
A TOPSIS Based Cluster Head Selection for Wireless Sensor Networks [5] | 2016 | TOPSIS algorithm and measures the (distance to BS, transmission rate, Residual energy) | Combined metric schemes | Improve the lifetime of the network compared to LEACH and AHP | Focusing on the accuracy degree at the expense of the new burdens that can be added in terms of mathematical complexity and increasing of signals |
Clustering Approach in Wireless Sensor Networks Based on K-means: limitations and Recommendations [26] | 2015 | Residual energy measuring method and the nodes’ threshold value | Adaptive schemes | Better nodes working in the network in comparison to the LEACH | Consumed power in the residual energy measuring and calculated it with the threshold value for all nodes, itself considered as an additional burden on the network |
Energy Efficient distributed cluster head scheduling scheme for two-tiered WSNs [25] | 2015 | Remaining energy/Cluster optimal centrality degree | Adaptive schemes | Overcome frequent election of cluster head and fair energy load balancing among cluster-based networks | The residual energy level measurement and signal strength to each node adds more burdens to the network, resulting in more energy consumed |
DCPVC Protocol [27] | 2015 | Calculating the remaining energy and the times of being elected as cluster head | Combined metric schemes | Energy consumption and better construction time | Calculating the remaining energy and election times as the cluster head for each sensor node adds more load to the network. |
Cluster head selection optimization based on genetic algorithm [28] | 2015 | Calculating the residual energy, inter-and intra-cluster distance | Adaptive scheme | Good lifetime and network load balancing | Repetitive residual energy and distances calculation adds more burdens, leading to whole-network delay and quick power depletion |
DCHEP [29] | 2015 | Remaining energy measuring and other sensors connectivity | Hybrid clustering scheme | (1) Scalable and flexible solution targeting intense WSNs with random mobility; (2) Achieves high growth in energy performance and availability compared to LEACH. | (1) Residual energy calculated for the nodes is unsuccessful in the case of the nodes’ rapid movement; (2) Resultsin continuous connectivity loss. |
The Energy Efficient Multi-Hop Clustering Process for Data Transmission in Mobile Sensor Networks [30] | 2014 | Energy level Measuring, Stability, Connectivity | Hybrid clustering scheme | (1) Introduces high bandwidth and low delay in the network; (2) Periodical cluster head election is enhanced to form effective routing and reduce energy consumption; (3) Good connectivity and stability. | (1) Periodically maintaining the routing table makes the security side weak; (2) Residual energy calculating of the nodes is often unsuccessful in rapid nodes movement, especially in the critical cases; (3) Delays due to the failure of some nodes. |
LEACH [31] | 2002 | A random CH election Adaptive, self-configuring cluster formation | Deterministic schemes | (1) Distributes power waste evenly across the sensors; (2) High scalability; (3) Robusts the security side; (4) Simple, low overhead, and fast. | (1) The random method adds more limitations; (2) Some nodes cause a waste of energy in transmitting data to an area too far from cluster heads; (3) High redundancy of data. |
Value | Parameter |
---|---|
100 × 100 m | Network size |
Sensor | Device Type |
100 | Number of sensor nodes |
1460 | MTU (bytes) |
DSR | WSNs Routing Protocols |
PHY IEEE802.15.4 | MAC Layer Protocol |
512 bits | Control packet size |
4000 bits | Data packet size |
6 J = 6000 m J | Initial Energy |
50 nj/bit | Eelect |
CARRIER_SENSE_ONLY | CCA |
−95 | Packet Reception Power Threshold (dBm) |
−85 | Receiver Sensitivity (dBm) |
100 | Sensor Range (m) |
250 | Data Rate (Kbps) |
1200 | Simulation Time (s) |
NetSim | Simulator |
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Yagoub, M.F.S.; Khalifa, O.O.; Abdelmaboud, A.; Korotaev, V.; Kozlov, S.A.; Rodrigues, J.J.P.C. Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks. Sensors 2021, 21, 5206. https://doi.org/10.3390/s21155206
Yagoub MFS, Khalifa OO, Abdelmaboud A, Korotaev V, Kozlov SA, Rodrigues JJPC. Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks. Sensors. 2021; 21(15):5206. https://doi.org/10.3390/s21155206
Chicago/Turabian StyleYagoub, Mudathir F. S., Othman O. Khalifa, Abdelzahir Abdelmaboud, Valery Korotaev, Sergei A. Kozlov, and Joel J. P. C. Rodrigues. 2021. "Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks" Sensors 21, no. 15: 5206. https://doi.org/10.3390/s21155206
APA StyleYagoub, M. F. S., Khalifa, O. O., Abdelmaboud, A., Korotaev, V., Kozlov, S. A., & Rodrigues, J. J. P. C. (2021). Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks. Sensors, 21(15), 5206. https://doi.org/10.3390/s21155206