A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems (1609.04069v1)

Abstract: Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

• The paper surveys multi-objective optimization (MOO) in wireless sensor networks (WSNs), detailing key metrics, algorithms, and open challenges in the field.
• The survey categorizes prevalent MOO methods into mathematical programming and nature-inspired metaheuristics, discussing their suitability for complex WSN problems.
• The research highlights the practical impact of MOO for efficient resource management in WSNs and identifies future directions including security, 3D networks, and hybrid algorithms.

A Survey of Multi-Objective Optimization in Wireless Sensor Networks (WSNs): Metrics, Algorithms, and Open Problems

Wireless Sensor Networks (WSNs) are a prominent technology used for monitoring and surveillance tasks due to their ability to collect vast amounts of data from disparate environments. However, optimizing WSN performance is challenging due to the trade-offs among various conflicting criteria, such as energy dissipation, packet-loss rate, coverage, and network lifetime. This paper presents an in-depth survey focused on multi-objective optimization (MOO) techniques applied to WSNs, shedding light on the metrics, algorithms, and open challenges in this domain.

Overview of Optimization Objectives in WSNs

The paper articulates the principal objectives in WSNs that typically require optimization. This includes maximizing network coverage, enhancing energy efficiency, prolonging network lifetime, ensuring reliable connectivity, minimizing latency, and maintaining operation under varying node densities with power and cost constraints. The complexity arises from the need to optimize these objectives simultaneously, as they often conflict with each other. For instance, extending network coverage might increase energy consumption, reducing the network's operational lifetime. Thus, MOO methods hold significant potential for yielding a Pareto-optimal set that represents the trade-offs between these conflicting metrics.

MOO Techniques and Algorithms

The survey categorizes prevalent MOO approaches into mathematical programming-based methods (e.g., linear weighted-sum, $\epsilon$-constraints, goal programming) and nature-inspired metaheuristics (e.g., evolutionary algorithms, swarm intelligence-based algorithms) along with discussions on other advanced techniques like fuzzy logic and reinforcement learning. Each method's suitability and performance vary depending on the specific scenarios and metrics selected for optimization. For example, scalarization methods are effective for linear objective transformations but may struggle with non-convex Pareto fronts, whereas metaheuristic algorithms like genetic algorithms and differential evolution provide robust solutions for complex, nonlinear problems.

Key Contributions and Numerical Results

The paper provides a comprehensive survey of recent studies focusing on MOO within WSNs, highlighting algorithms that achieve efficient trade-offs. The use of hybrid algorithms, such as the combination of particle swarm optimization with fuzzy logic, is emphasized for achieving superior coverage, connectivity, and lifetime metrics. Additionally, real-world applications of these methods demonstrate their efficacy in scenarios demanding high reliability and efficient resource management.

Theoretical and Practical Implications

From a theoretical standpoint, the surveyed methods offer frameworks for tackling complex MOPs. Practically, these algorithms enable the deployment of WSNs in environments where traditional single-objective optimization would fail to provide satisfactory solutions. They are particularly impactful in environmental monitoring, smart grids, and IoT applications where resources are often limited, and operational longevity is critical.

Open Problems and Future Prospects

The paper concludes by identifying several open research areas in the field of MOO for WSNs. These include addressing security concerns in dynamic and hostile environments, optimizing multi-hop and three-dimensional network configurations, and integrating cognitive radio capabilities for better spectrum management. Additionally, leveraging game theory and hybrid computational intelligence methods could furnish new insights into the optimization of WSNs.

In summary, while significant advances in MOO methods for WSNs have been made, further research is required to address the evolving challenges posed by new application areas and more demanding operational environments. This survey serves as a pivotal reference for researchers intending to explore the intricacies of MOO in WSNs, fostering innovation and improvement in practical deployments.