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Open research questions in Energy Efficient Wireless Sensor Networks

48 unresolved questions extracted from the limitations and future-work sections of 355 Energy Efficient Wireless Sensor Networks papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The limited energy capacity of sensor nodes presents a significant challenge to WSN longevity and reliability - Traditional static routing methods are inefficient in terms of energy consumption

    Optimizing Energy Efficiency in Wireless Sensor Networks Using Adaptive Routing Algorithms · 2026 · DOI

  • To implement the proposed algorithm in a real-world scenario. To compare the proposed algorithm with other existing algorithms. To improve the proposed algorithm for better energy efficiency and network lifetime.

    Analysis Of Spatial Correlation Based Multi-Hop Routing Scheme With Varying Nodal Energy Routing Agents For Wsns · 2026 · DOI

  • The existing approaches do not consider the spatial-correlation between sensors for clustering and data routing. The existing approaches do not select cluster heads based on their energy reserve.

    Analysis Of Spatial Correlation Based Multi-Hop Routing Scheme With Varying Nodal Energy Routing Agents For Wsns · 2026 · DOI

  • The hotspot problem in WSNs deployed in narrow linear topologies. Energy depletion and network fragmentation. Limited scalability and message efficiency of existing protocols.

    LHUCR: an energy-aware hierarchical unequal clustering and hybrid routing protocol for monitoring sewage pipe networks · 2026 · DOI

  • The lack of a proper way to choose the right cluster heads and guarantee sound data transmission in AUV-assisted underwater sensor networks. The need for energy-efficient clustering and routing protocols in underwater sensor networks.

    Energy-Efficient Clustering and Routing ProtocolUsing GAN-Based Fuzzy Clustering for AUVAssisted UWSNs · 2026 · DOI

  • More extensive testing may be performed to validate the claim. A detailed examination of the trade-offs and computational complexity of the proposed approach would be helpful. The algorithm can be further improved to handle more complex scenarios.

    An Enhanced LEACH-Based Dynamic Routing Framework for Energy-Efficient Heterogeneous Wireless Sensor Networks · 2026 · DOI

  • The existing LEACH protocol has limitations in terms of energy efficiency. There is a need for a more efficient routing protocol for heterogeneous WSNs.

    An Enhanced LEACH-Based Dynamic Routing Framework for Energy-Efficient Heterogeneous Wireless Sensor Networks · 2026 · DOI

  • Uneven energy depletion among nodes. Limited adaptability and fault recovery capabilities. High reconfiguration overhead.

    Graph-Theoretic Optimization of Backbone Structures in Wireless Sensor Networks Using Domination Models · 2026 · DOI

  • This paper introduces the RL-SHR, a Reinforcement Learning based Self-Healing Routing protocol to enhance the fault tolerance and energy efficiency of Wireless Sensor Networks. The RL-SHR protocol employs the Q-Learning algorithm at each node to facilitate a decentralized, adaptive routing mechanism according to real-time observations of link quality, residual energy, and delivery success. The proposed protocol includes a reward-driven learning model and a self-healing module to ensure reliable communication in the event of node and link failures. Simulation experiments have clearly shown that the RL-SHR protocol outperforms traditional routing protocols such as AODV and DSR in several performance aspects. In particular, the RL-SHR protocol maintained a packet delivery ratio of above 85% even at a 50% 122 Volume 18 (2026), Issue 2 Reinforcement Learning-Based Self-Healing Routing in Fault-prone Wireless Sensor Networks node failure rate, while AODV and DSR decreased to below 70% and 60%, respectively. Furthermore, the RL-SHR protocol reduced the end-to-end delay and energy consumption by adaptively avoiding congested and low-energy nodes. In the aspect of network lifetime, the RL-SHr protocol improved the time to first node death and 50% nodes death compared to AODV by more than 25% in different scenarios. Moreover, the convergence analysis indicates that the RL- SHR protocol converges very quickly to its Q-values, ensuring efficient routing operations over time, which is not feasible in traditional reactive routing protocols. These comparative observations reaffirm that RL-SHR provides a significant performance benefit over non-learning- based solutions, particularly within dynamic and failure-prone WSN settings. Although AODV and DSR routing solutions are based on static path discovery and maintenance, the RL-SHR approach dynamically learns and adapts, thus being more robust, energy-efficient, and scalable. This research effort makes a contribution towards making WSNs more robust, autonomous, and amenable for mission-critical applications. Although the performance benefits in terms of packet delivery, delay, energy efficiency, and network lifetime have been shown to be significant, there exist some limitations. The existing performance assessment has been carried out on networks of moderate size; scalability to thousands of nodes has not been investigated yet, and it may cause convergence-related delays and increased computational complexity. Furthermore, the existing investigation has considered a mostly static network topology, and mobility-related WSN settings, such as vehicular or UAV-based WSNs, have not been investigated adequately.Addressing these aspects will be crucial for extending RL-SHR to large-scale and mobile deployments. Future work will therefore investigate scalability optimizations, mobility-aware learning strategies, and real-world validation on hardware testbeds. The future work will also explore extensions such as mobility aware RL models, the integration of deep reinforcement learning e.g., DQN, and cooperative multi agent learning to further enhance convergence & scalability.

    Reinforcement Learning-Based Self-Healing Routing in Fault-prone Wireless Sensor Networks · 2026 · DOI

  • Future research can focus on evaluating the proposed MHHV-DL model in real-world scenarios. The model can be further improved by incorporating additional optimization techniques.

    Design and Implementation of Energy-Efficient Routing Protocols for Maximizing Lifetime in Wireless Sensor Networks · 2026 · DOI

  • Traditional routing algorithms have limitations in terms of energy efficiency and network lifetime. There is a need for a novel energy-efficient routing protocol that can maximize network operation time.

    Design and Implementation of Energy-Efficient Routing Protocols for Maximizing Lifetime in Wireless Sensor Networks · 2026 · DOI

  • Future research can focus on evaluating the LTAWSN algorithm in large-scale networks. The algorithm's performance in real-world scenarios can be evaluated.

    Optimized routing with Ant Colony Algorithms to extend network lifetime in Wireless Sensor Networks · 2026 · DOI

  • Energy-efficient routing in WSNs is a critical challenge due to uneven energy depletion and dynamic topology changes. Traditional routing algorithms do not effectively address this challenge.

    Optimized routing with Ant Colony Algorithms to extend network lifetime in Wireless Sensor Networks · 2026 · DOI

  • Future research can focus on implementing the proposed protocol in real-world scenarios. Further research can be conducted to improve the energy efficiency of the protocol and reduce energy holes.

    Energy-efficient wireless sensor networks using unequal clustering and cluster head rotation optimization · 2026 · DOI

  • The energy hole problem is a significant challenge in wireless sensor networks. Existing solutions have limitations, including high energy consumption and reduced network lifespan.

    Energy-efficient wireless sensor networks using unequal clustering and cluster head rotation optimization · 2026 · DOI

  • Existing approaches have limitations in terms of adaptability, fault recovery, and uneven energy depletion. Conventional single Connected Dominating Set-based approaches are not sufficient.

    Graph-Theoretic Optimization of Backbone Structures in Wireless Sensor Networks Using Domination Models · 2026 · DOI

  • Testing the framework in real-world environments. Improving the computational efficiency and feasibility of the framework. Applying the framework in various domains and applications.

    Machine Learning-driven Energy-efficientRouting in Wireless Sensor Networks: PredictingNode Lifetime for Optimized Performance · 2026 · DOI

  • Traditional routing protocols have limitations in addressing energy efficiency. There is a need for a scalable and energy-efficient next-generation WSN solution.

    Machine Learning-driven Energy-efficientRouting in Wireless Sensor Networks: PredictingNode Lifetime for Optimized Performance · 2026 · DOI

  • Investigation of security-aware routing and fault tolerance. Development of more secure and multi-objective optimization strategies. Integration of Machine Learning, Edge Computing, and UAV-assisted routing.

    Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions · 2026 · DOI

  • Existing reviews often focus on isolated network layers or outdated datasets. Lack of comprehensive reviews on metaheuristic approaches for energy optimization in WSNs.

    Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions · 2026 · DOI

  • Future research can focus on developing new clustering algorithms that can optimize energy consumption and network lifetime in WSNs - Future research can also focus on evaluating the performance of various clustering algorithms in different scenarios

    Comprehensive Survey on Cluster based Energy Efficient Routing Protocols in Wireless Sensor Networks · 2026 · DOI

  • There is a need for a comprehensive survey of cluster-based routing protocols for WSNs - There is a lack of understanding of the advantages and limitations of various clustering algorithms

    Comprehensive Survey on Cluster based Energy Efficient Routing Protocols in Wireless Sensor Networks · 2026 · DOI

  • Development of energy-efficient routing approaches for WSNs is crucial task. Static routing may lead to total network failure when a node is depleted.

    Performance Comparison of Energy Aware Routing Techniques in Wireless Sensor Networks · 2026 · DOI

  • Existing trust-aware and energy-efficient WSN-IoT routing protocols have limitations. They consider trust computation, CH selection, and secure routing independently.

    ETBORC: Energy and Trust-aware Bio-inspired Optimized Routing Based on Clustering for Securing Wireless Sensor Network · 2026 · DOI

  • The proposed scheme introduced the broad trust-aware clustering and secure routing frame- work for the large-scale WSN in the IoT environment. It integrates trust computation and bio-inspired optimization techniques. This model helps to evaluate the reliability of the node through direct and indirect trust. It considers residual energy and ensures that only trustworthy and energy-efficient nodes participate in CH selection to forward data. The SHO algorithm has been implemented to choose the optimal CH. It includes the trust, energy, and coverage in the multi-objective fitness function, which results in balanced cluster forma- tion and reduced energy. CSA has been implemented to determine secure multi-hop routing paths from CHs to the base station. It chooses the high-energy and high-trust nodes with the minimum number of hop counts. These operations are all managed by the decision manager. The SHO-based clustering and CSA-based routing reduce the communication overhead, balance the energy consumption, and improve network scalability. The adaptive trust com- putation and decision manger enables the proposed framework to respond effectively to changing network conditions, node failures, and malicious activities. The energy manager regularly updates the energy state of the network to be adaptable the proposed ETBORC improves the packet delivery ratio and increases the resistance to the malicious or selfish nodes. It also helps to prolong the network lifetime by balancing the energy consumption Implementation of cross-layer optimization provides a robust and intelligent communica- tion system that is suitable for practical WSN-based IoT applications such as smart cities, environmental monitoring, healthcare systems, industrial automation, precision agriculture, disaster management, and intelligent transport system. Despite the many advantages, the system increases in the computational complexity while integrating the multiple optimiza- tion modules. In the future research will focus on developing lightweight optimization tech- niques, that incorporate the machine learning and deep reinforcement learning for adaptive trust and routing decisions, supporting the heterogeneous and mobile IoT environments, and validating the framework using the real-world experimental setup to further enhance scal- ability, robustness and practical deployment. Author Contributions Amruta Veerendra Pattar worked on preparing manuscript, algorithm design, and implementation, Selvi M worked on algorithm design, supervising, proof read the manuscript. Funding Availability: No Funding Available. Data Availability Data are available and it will be shared upon request. 1 3A. V. Pattar, M. Selvi Declarations Competing Interests The authors declare no competing interests. Ethics Approval Authors provide the ethical approval for the given manuscript.

    ETBORC: Energy and Trust-aware Bio-inspired Optimized Routing Based on Clustering for Securing Wireless Sensor Network · 2026 · DOI

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48 open questions have been extracted from the limitations and future-work passages of 355 Energy Efficient Wireless Sensor Networks papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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