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Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey
Abhilash Singh, Sandeep Sharma, Jitenda Singh
TL;DR
Wireless Sensor Networks face energy constraints, sensing-accuracy challenges, and redundant information while prior work addressed energy and node placement separately. This paper reviews nature-inspired optimization for coverage and compares IGABACA with Lion Optimization, finding faster coverage and fewer iterations for LO.
Problem
WSNs must address limited sensor battery lifetime, accurate sensing, redundant information, and the lack of collective solutions for energy constraints and node placement.
Method
The paper reviews optimization algorithms for WSN issues and evaluates two meta-heuristic approaches for optimal sensor-node placement and coverage.
Results
Lion Optimization provides faster network coverage than IGA-BACA and achieves optimal coverage in fewer iterations.
Takeaways & Limitations
The review and comparison provide a basis for exploring nature-inspired algorithms for WSN coverage and related applications.
Takeaways & Limitations
The paper identifies scope for further exploration of Lion Optimization and its application to multi-objective problems.
Abstract
from arXiv · showhide
In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. Keywords: Optimal Coverage, Bio-inspired Algorithm, Lion Optimization, WSNs.
1. Introduction
The paper frames WSN coverage as a trade-off between conserving limited sensor energy and avoiding redundant sensing while preventing coverage holes. It reviews nature-inspired optimization and compares IGA-BACA with Lion Optimization for multi-objective coverage.
- Motivation: WSN deployment must balance energy constraints, sensor separation, redundant information, and uncovered coverage holes.Placing sensors too close increases overlap, whereas placing them too far apart can create blind areas.
- Research gap: Existing studies often address energy constraints and node placement separately rather than collectively.The paper therefore formulates them as a multi-objective optimization problem.
- Research objective: Optimal coverage aims to reduce sensor use while covering the target area, supporting lower resource wastage.The proposed optimization seeks optimal coverage with fewer sensor nodes.
- Related work: Prior reviews cover nature-inspired algorithms in WSNs, but few critically examine optimal coverage and WSN problem domains together.The paper positions its review as addressing this coverage-specific gap.
- Comparison: The study compares IGA-BACA, a hybrid evolutionary and swarm-based algorithm, with LO, a purely swarm-based algorithm.Both are evaluated for the stated multi-objective optimization problem.
2. WSNs and Optimizations
Wireless sensor networks face interacting challenges involving energy efficiency, QoS, security, coverage, aggregation, routing, and localization. The paper surveys optimization approaches across these domains, emphasizing coverage and nature-inspired algorithms.
- WSN challenges: WSNs face trade-offs among energy efficiency, QoS, and security, motivating simultaneous optimization rather than isolated treatment.The paper proposes multi-objective optimization as one way to address these interacting issues.
- Clustering and Routing: Other WSN optimization domains include energy-efficient clustering and routing, where objectives include high-energy cluster-head selection, route selection, lifetime, and communication distance.Routing commonly groups sensors, selects cluster heads, and forwards collected data to the sink.
- Optimal Coverage in WSNs: Optimal coverage seeks to cover an entire target area with as few sensors as possible.Sensor placement can reduce overlapping sensing regions, redundant information, and battery waste.
- Optimal Coverage in WSNs: Coverage models include hexagonal, circular, and irregular real-life sensing shapes, with circular models trading low complexity against coverage holes and overlap.Increasing circular sensing radius can compensate for coverage holes but may create overlapping regions.
- Data Aggregation in WSNs: Data aggregation reduces redundant sensing by combining information through tree-, cluster-, grid-, or chain-based structures.These structures aggregate data at intermediate, cluster-head, grid, or lead nodes before forwarding it.
- Sensor Localization in WSNs: Sensor localization estimates unknown-node positions in two phases: distance estimation followed by position calculation.The paper identifies localization error minimization and unknown-node accuracy as key challenges.
3. Theoretical Background of the Leading Algorithms in WSNs Arena
The section formalizes nature-inspired algorithms as iterative mappings and outlines PSO, GA/IGA, ACO/BACA, and LO-inspired search mechanisms for WSN optimization.
- Mathematical foundation: Nature-inspired optimization algorithms can be represented as iterative processes that map current solutions to better solutions using parameters and random variables.The mapping φ connects the solution at time t to the solution at time t + 1.
- Mathematical foundation: Population-based systems extend the single-agent formulation to n swarm solutions while retaining algorithm-dependent parameters and randomization.The population formulation applies the transition to multiple candidate solutions.
- PSO: PSO randomly initializes particles and iteratively updates their velocities and positions using individual and collective experience.Confidence factors c1 and c2 represent private particle thinking and collaboration among particles, while r1 and r2 are random values between [0,1].
- GA and IGA: GA encodes solutions as chromosomes, evaluates fitness, selects stronger solutions, and applies reproduction, crossover, and mutation.Crossover exchanges genetic material, while mutation searches for unexplored genetic material using probability Pm.
- GA and IGA: IGA adaptively changes crossover and mutation probabilities according to individual conditions to retard premature convergence.The constants governing Pc and Pm are constrained so these probabilities remain within [0,1].
- ACO and BACA: BACA uses binary path selection and pheromone updates, while IGA-BACA initializes BACA pheromone information with the optimized result of IGA.BACA differs from conventional ant colony optimization in how ants select paths, and binary coding is reported to increase efficiency.
- Lion Optimization: Lion Optimization models cooperative hunting, in which lions encircle prey and update positions when prey escapes.The new prey position depends on the current prey position and the hunter’s percentage of fitness improvement.
4. Solution to the Problem Domains and Present Status
The paper surveys prominent bio-inspired meta-heuristic solutions for WSN problem domains, focusing on PSO, GA, and ACO.
- Scope: The survey summarizes WSN solutions based on PSO, GA, and ACO across multiple problem domains.These algorithms are presented as prominent bio-inspired meta-heuristic approaches for WSNs.
4.1. Applications of PSO in WSNs
PSO has been applied across WSN coverage, deployment, localization, clustering, routing, data aggregation, power allocation, and sensor management problems.
- Coverage and core applications: PSO supports near-optimal WSN coverage, coverage-hole minimization, data aggregation, energy-aware cluster-head selection, and sensor localization.The survey also identifies PSO applications for high-energy cluster-head selection and localization-error minimization.
- Deployment and positioning: PSO variants address dynamic deployment, Voronoi-based coverage, base-station positioning, large-radius sensor deployment, and topology-link reduction.Reported applications include mobile sink placement and avoiding overlap between coverage cells.
- Modified PSO: Modified PSO methods target high-dimensional underwater deployment, optimal sensor deployment, premature-convergence reduction, and co-evolutionary optimization.PSGO incorporates GA selection and mutation operators to address premature convergence in PSO.
- Localization: PSO localization studies report better accuracy than Gauss-Newton and simulated annealing, while bacterial foraging is reported as more accurate than PSO but slower.The comparison distinguishes speed from accuracy across localization methods.
- Clustering and routing: PSO is used for energy-efficient clustering and routing, including dense mobile-sensor clustering, multi-hop clustering, base-station repositioning, and network-lifetime objectives.The survey reports reduced energy consumption and identifies a supervisor-student PSO model as outperforming three other PSO variants.
- Other applications: PSO applications also include multi-objective data aggregation, constrained power allocation, hybrid ACO-PSO sensor management, and penalty-function optimization.The surveyed approaches address accuracy, time, dynamic management, and constrained optimization in WSNs.
4.2. Applications of GA in WSNs
GA is surveyed as a flexible WSN optimizer for deployment, coverage, localization, clustering, routing, data aggregation, and network-lifetime objectives.
- General applications: GA supports random and deterministic deployment, reduced data-aggregation points, pre-clustering, shorter communication distance, and accurate node localization.Its global-search capability is associated with higher localization accuracy in the surveyed applications.
- Coverage and deployment: GA-based coverage studies include multi-objective deployment and power assignment, large-network deployment, fixed deployment, and energy-efficient coverage.Reported outcomes include balanced performance and high network coverage rate.
- Coverage and deployment: IGA-BACA is used for optimal WSN coverage and is reported to outperform conventional GA with a high coverage rate.The approach is also associated with reduced sensing of redundant information.
- Localization: GA-based localization methods include DV-Hop GA and DMA-integrated GA, with reported improvements over earlier algorithms in accuracy and energy consumption.The DMA-GA combination targets accurate node localization in WSNs.
- Clustering and routing: GA is applied to energy-efficient clustering and routing through dynamic clustering, two-dimensional location-aware clustering, quantum GA, and multi-objective QoS routing.The surveyed studies report improvements over earlier schemes and reductions in average power consumption.
- Data aggregation and lifetime: GA-based spanning-tree and grid-based aggregation schemes are reported to reduce power consumption and improve network lifetime.Later variants use residual node energy to further improve network lifetime.
4.3. Applications of ACO in WSNs
ACO approaches address multiple WSN problem domains, including deployment, localization, routing, clustering, and data aggregation. Reported applications commonly target coverage, energy use, network lifetime, localization accuracy, or load balancing.
- Deployment and coverage: ACO-based approaches support dynamic sensor deployment and near-optimal coverage, particularly in large and dynamic WSNs.
- Deployment and coverage: ACO deployment methods have been formulated as a multiple knapsack problem and reported complete coverage with prolonged network lifetime.
- Localization: ACO has been applied to node localization using beacon signals, interval-theory prediction with relocation, trilateration-error optimization, and mobile anchors.
- Routing and clustering: ACO-based routing and clustering methods report lower power consumption, improved load balancing, reduced packet loss, and increased network lifetime.
- Data aggregation: ACO has also been used for data aggregation, with reported improvements in energy efficiency, stability, accuracy, and network lifetime.
- Algorithm status: The paper introduces LO alongside established PSO, GA, and ACO approaches as a nature-inspired method for WSN problems.
5. Optimal Coverage using IGA-BACA and LO
Optimal WSN coverage is formulated as a multi-objective problem balancing maximum covered area against minimum working sensors. The paper applies IGA-BACA and LO to optimize this trade-off using binary sensor-selection representations.
- Optimization objective: Optimal coverage seeks a sensor subset that maximizes monitoring-area coverage while minimizing the number of working sensors.
- Optimization objective: Because coverage and sensor minimization conflict, the paper combines them into a maximal objective function for optimization.
- Sensor representation: Binary control vectors encode each sensor as active or inactive, with corresponding algorithm individuals linked one-to-one to selected nodes.
- Optimization algorithms: IGA-BACA uses reproduction, selection, crossover, mutation, and pheromone updating, whereas LO uses mating, sorting, and elimination.
- IGA-BACA process: Crossover and mutation restore solution variety after fitness-based selection removes lower-fitness offspring.
- LO process: LO mates the best nomad lions, ranks the population by fitness, and eliminates the least-fit nomad.
6. System Model
The system model treats coverage as a reliability- and QoS-sensitive optimization problem in a two-dimensional, grid-partitioned monitoring area. Bio-inspired algorithms select an active sensor set that balances coverage and sensor count.
- System motivation: Coverage holes affect the QoS threshold, network coverage rate, and network reliability.
- System motivation: Adding sensors to critical areas can increase reliability but also increases network cost.
- System objective: The paper uses bio-inspired algorithms to find an optimal node set under this coverage-cost trade-off.
- Network model: The monitoring area is modeled as a two-dimensional plane divided into m × n equal grids, with N randomly distributed sensors.
- Coverage model: Each sensor has sensing radius r and coordinates (x_i, y_i), while target points are covered when their distance from a sensor is at most r.
- Objective function: The objective combines network coverage and the proportion of active sensors into a multi-objective optimization problem maximized to obtain coverage with fewer nodes.
7. Simulation Results
The simulations compare IGA-BACA and LO on coverage, processing time, and active-sensor count across generations. Both approaches reach optimal coverage, but LO does so with fewer generations and fewer sensors while converging faster.
- Benchmark setup: 42 of 100 sensors covered the monitoring area optimally after 300 IGA-BACA iterations, providing the benchmark for further analysis.
- Random deployment: Random deployment produced redundant nodes and coverage holes, so random network coverage was not usually adopted.
- IGA-BACA results: Increasing IGA-BACA generations from 50 to 200 moved coverage toward the optimum and significantly reduced redundant nodes.
- Algorithm comparison: Both IGA-BACA and LO obtained optimal network coverage, but IGA-BACA required approximately 300 iterations whereas LO required 250.
- Evaluation measures: The evaluation tabulated network coverage rate, GPU processing time, and active-sensor count at 50, 100, 150, 200, and 250 iterations.
- Algorithm comparison: LO achieved optimal coverage with fewer sensors and exhibited a faster convergence rate than IGA-BACA.
8. Conclusion
The paper reviews nature-inspired optimization for WSN problems and compares IGA-BACA with LO for optimal coverage. LO achieves faster coverage improvement and reaches optimal coverage in fewer iterations, while the authors identify multi-objective optimization as an area for further exploration.
- The paper presents a comprehensive review of nature-inspired algorithms applied to issues related to WSNs.
- It evaluates and compares IGA-BACA and LO for computing optimal coverage in WSNs.
- As iterations increase, the network coverage rate tends toward optimal coverage for both approaches.
- LO has a faster network coverage rate than IGA-BACA.
- LO achieves optimal coverage in fewer iterations, attributed to many local maxima with higher fitness values.
- The authors suggest exploring LO further, including its application to multi-objective problems and combination with machine-learning approaches such as ANN.
CRediT author statement
The author statement assigns conceptualization, methodology, software, data curation, writing, visualization, investigation, supervision, validation, and editing across the listed authors.
- Singh and Sharma contributed conceptualization, methodology, and software.
- Sharma contributed data curation, original-draft preparation, visualization, and investigation.
- Sharma provided supervision, while Singh, Sharma, and Jitendra Singh contributed software and validation.
- Singh, Sharma, and Jitendra Singh contributed writing, reviewing, and editing.