Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance
- Environment monitoring: Since the attention on sustainable energy solutions has increased substantially, smart technologies for energy conservation have gained importance. Monitoring environmental conditions have been one of the popular applications of WSN that controls and manages services such as air, water, and soil monitoring [23,24]. This network allows the end-user to gather data at a resolution located in areas where accessing data is otherwise difficult. Moreover, long-term monitoring is required to obtain sufficient information to decide.
- Home Application: With the expansion of WSN integrated IoT, tiny sensor nodes can be implanted into household equipment such as electric appliances and furniture to access from a remote place . For instance, the nodes can be incorporated into microwave ovens, vacuum cleaners, washing machines, and even air conditioners that can be connected to a room server. Such arrangements are self-manageable and require minimal human interventions to frame a smart home structure.
- Healthcare Application: Driven by the convergence of data collection regarding the health of people and maintaining accuracy in collected information with minimal cost, WSN for healthcare has evolved in recent years . Researchers have invented a new branch of WSN exclusive for healthcare, called Body Area Network (BAN), that provides better treatment at a lesser cost. BAN is administered to monitor patients’ physiological, cognitive, and psychological information.
- Tracking and Military Application: Battlefield surveillance was a wireless medium’s most critical application of sensor networks. Characteristics such as self-association, fast organization, and well adaptation to node failures make WSN an extremely desired sensing technique for military applications . Sensor nodes could be located in remote areas to sense and receive data as long as possible that can alert the user about possible blast location, enemy positions, and chemical attacks.
- Transportation: WSN helps drivers alert any congestion or traffic problem by regularly monitoring traffic statistics. Vehicular motions can be tracked instantaneously to avoid any blockage or accidents . WSN also reduces the length of the wiring harness and saves time with the cost of its installation.
- Industrial Applications: WSNs offer momentous cost reductions and investments that allow innovative functionalities for industry-based technologies. Continuous sensing of the environment, condition monitoring, and process automation are some of the requirements of Industrial WSN . The solutions need to be simple to use and install yet versatile with low-cost devices and a long lifetime.
1.1. Related Work
1.2. Organization of the Paper
2.1. Energy Consumption in WSN
2.2. Clustering Strategies in WSN
- Deterministic: Here, the CHs are set at fixed positions in the network . The sensors broadcast a HELLO message to their neighbors, and the node that first receives the maximum number of these messages is elected as CHs and initiates the cluster formation phase. The important attributes of these clustering schemes are node identity numbers (IDs) and node degree (number of neighboring nodes).
- Adaptive: Instead of random CH selection, adaptive clustering schemes are based on the selection of CH considering particular parameters, such as remnant energy, the distance between nodes, energy dissipated in the last round, and distance to BS . Specific combinations of these parameters form the objective function for CH selection that can adapt to the rapid variations in the network. Adaptive schemes can be further categorized as BS-assisted or probabilistic (self-organized) based on who has the power to initiate the CH selection process. Again, considering the parameters for the role of a sensor node, the probabilistic scheme can be classified as resource adaptive and fixed parameters.
- Hybrid: This clustering strategy considers combined clustering metrics with other architectures to increase energy efficiency.
3. Classical Routing Protocols
3.1.1. Advantages of LEACH
- The concept of clustering in LEACH effectively increases the network lifetime;
- Traffic reduction due to aggregation of data at the cluster head level;
- One-hop data routing from node to CH reduces energy consumption;
- Global knowledge of the network is not required as LEACH is a distributed protocol, and BS does not control nodes in cluster formation.
- Intra-cluster collisions are avoided due to the allocation of TDMA slots for data transfer.
3.1.2. Disadvantages of LEACH
- Due to the random selection of CH, the probability of CH election remains the same for each node, indicating that the probability of a node with high residual and low residual energy remains the same . However, if a low residual energy node gets elected as CH, it will eventually limit the network lifetime to a short span;
- The number of CHs in each round varies randomly in LEACH. Moreover, the position of CH is not fixed and may be centrally or boundary placed . This will eventually contribute to extra energy consumption in cluster-level communication and degrade network performance;
- CHs have responsibilities such as data collection, aggregation, and transfer to BS. Hence, it is very likely that the CHs will deplete energy faster than other nodes . Moreover, if a CH fails, all the member nodes connected to it will run out of power, resulting in a broken network;
- The CHs communicate with the BS in a single-hop mode, making LEACH not preferably used in large-scale wireless sensor networks .
3.2. Modification of LEACH Based on CH Selection
3.2.3. Solar-Aware Distributed LEACH
3.2.4. Multi-Hop LEACH
- If this distance is greater than or equal to the minimum distance between every CH and the next (MDCH), then the second-highest T(n) node is declared as CH.
- If this distance is lesser than the minimum distance between every CH and the next (MDCH), the BS does not select any of these nodes as CH.
3.2.15. Enhanced LEACH
3.2.17. AvgRLEACH and VarRLEACH
4. Advent of Bio-Inspired Algorithms
4.5. Tabu PSO
4.8. KH Algorithm
4.10. SMOCH (Spider Monkey Optimized CH Selection)
- Network Lifetime: The precious resource of WSN nodes is energy, and the lifetime of WSNs is completely dependent on the amount of energy the nodes contain. The network lifetime is defined as the total time the network is fully operative until the last nodes run out of energy. It depicts the total time of network operation.
- Network Throughput: It is an important parameter that estimates the overall successful data transfer rate.
- Residual Energy: Every device on the network requires a certain amount of energy to carry out various network activities. The total energy consumption is calculated as the total sum of energy used by all the nodes in the network. If it is assumed that nodes consume energy while sending data, then the total energy consumed is equal to the total number of packets sent in the network. To be more precise, the residual energy represents the overall network energy level after the operation is complete.
- Packet Delivery Ratio (PDR): PDR is the ratio between the total packets sent from the source to the number of packets received at the destination. The network performance is better for higher PDR values.
- Scalability: It is a key criterion of WSN studied to observe the impact of network size on network lifetime. As the routing of data in a large-scale WSN is conducted with nodes having limited resources, the routing process becomes a very challenging issue. Hence, an efficient and scalable routing protocol design becomes essential to constrain such limitations.
5.1. Comparison of LEACH, A-LEACH, and LEACH DCS
5.2. Comparison of LEACH, I-LEACH, and MOD-LEACH
5.3. Comparison of LEACH, LEAC-GA, and LEACH-KH
6. Conclusions and Future Perspective
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Daanoune et al. , 2021||Classified LEACH-based protocols based on CH selection, data transmission, and both CH selection and data transmission techniques||Discusses the strength and weaknesses of LEACH-based protocols but fails to study bio-inspired LEACH variants|
|Bhagat et al. , 2020||Reviews LEACH successors based on single-hop and multi-hop models||Categorizes LEACH protocols based on the path of data transmission from CH to BS|
|Guleria et al. , 2019||Classification is conducted based on classical and swarm-intelligence approaches||Handling sink mobility, QoS requirement, and overhead due to dynamic topology in an energy constraint environment are still major issues|
|Prasad et al. , 2018||Analysis of different hierarchical routing protocols that are modified from LEACH based on energy efficiency only||Does not provide any information about the method or modification in detail|
|Singh et al. , 2017||Classification of LEACH successors based on single-hop and multi-hop communication is conducted, and comprehensive studies have been provided||The survey presents a detailed summary but fails to inform about the simulation or testing tool, or software used in network analysis|
|Sabor et al. , 2017||Survey on classical-based and optimized-based routing protocols for mobile WSNs||Real-work implantations, overheads, and computational time have not been explored|
|Arora et al. , 2016||LEACH and its extensions were discussed and compared that prolong network lifespan. The survey is not limited to proactive protocols and also focuses on reactive protocols such as TEEN , APTEEN , and TSEP ||The authors prove enhanced network lifetime with increased network area and distance to BS. However, the comparison conducted considered only a few protocols and variants of LEACH|
|Mahapatra et al. , 2015||The timeline of LEACH and its descendants are compared based on selected features such as scalability and mobility||Compares considering a few parameters|
|Dhawan et al. , 2014||Performance comparisons between LEACH protocol variants are made||Less number of examples discussed|
|Tyagi et al. , 2013||Highlights advantages and disadvantages of various routing protocols inspired by LEACH where CH selection plays an important role.||A brief comparison in terms of the CH selection process, load balancing, routing, and security|
|Bhattacharjee et al. , 2013||Performance measurements of energy-efficient protocols conducted||Improvement regarding application-specific deployment of nodes needs to be conducted|
|X Liu , 2012||A comprehensive survey of clustering routing protocols is conducted||Gives an idea about possible future research directions|
|Ramesh , 2012||Comparative study of CH selection algorithm||Scalability and stability factors are not explored|
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Behera, T.M.; Samal, U.C.; Mohapatra, S.K.; Khan, M.S.; Appasani, B.; Bizon, N.; Thounthong, P. Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance. Electronics 2022, 11, 2282. https://doi.org/10.3390/electronics11152282
Behera TM, Samal UC, Mohapatra SK, Khan MS, Appasani B, Bizon N, Thounthong P. Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance. Electronics. 2022; 11(15):2282. https://doi.org/10.3390/electronics11152282Chicago/Turabian Style
Behera, Trupti Mayee, Umesh Chandra Samal, Sushanta Kumar Mohapatra, Mohammad S. Khan, Bhargav Appasani, Nicu Bizon, and Phatiphat Thounthong. 2022. "Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance" Electronics 11, no. 15: 2282. https://doi.org/10.3390/electronics11152282