Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D

Wireless sensor networks design requires high quality location assignment and energy efficient power assignment for maximizing the network coverage and lifetime. Classical deployment and power assignment approaches optimize these two objectives individually or by combining them together in a single objective or by constraining one and optimizing the other. In this article a multi-objective deployment and power assignment problem (DPAP) is formulated and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. Following the MOEA/D's framework the above multiobjective optimization problem (MOP) is decomposed into many scalar single objective problems. The sub-problems are solved simultaneously by using neighborhood information. Additionally, unique problem-specific, parameter-rising, genetic operators and local search heuristics were designed specifically for the DPAP. In addition, a new encoding scheme is designed to represent a WSN based on the DPAP's design variables. Simulation results show that MOEA/D provides a high quality set of alternative solutions without any prior knowledge on the objectives preference.

The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a popular algorithm for solving Multi-Objective Problems (MOPs). The main component of MOEA/D is to decompose a MOP into easier sub-problems using a set of weight vectors. The choice of the number of weight vectors significantly impacts the performance of MOEA/D. However, the right choice for this number varies, given different MOPs and search stages. We adaptively change the number of vectors by removing unnecessary vectors and adding new ones in empty areas of the objective space. Our MOEA/D variant uses the Consolidation Ratio to decide when to change the number of vectors and to decide where to add or remove these weighted vectors. We investigate the effects of this adaptive MOEA/D against MOEA/D with a poorly chosen set of vectors, a MOEA/D with fine-tuned vectors and MOEA/D with Adaptive Weight Adjustment on two commonly used benchmark functions. We analyse the algorithms in terms of hypervolume, IGD and entropy performance. Our results show that the proposed method is equivalent to MOEA/D with fine-tuned vectors and superior to MOEA/D with poorly defined vectors. Thus, our adaptive mechanism mitigates problems related to the choice of the number of weight vectors in MOEA/D, increasing the final performance of MOEA/D by filling empty areas of the objective space and avoiding premature stagnation of the search progress.

The recent advances in embedded software/hardware design have enabled large-scale and cost-effective deployment of Wireless Sensor Networks (WSNs). Such a network consists of many small sensor nodes with sensing, control, data processing, communications, and networking capabilities. The wireless sensor networks have a broad spectrum applications ranging from wild life monitoring and battlefield surveillance to border control and disaster relief and have attracted significant interests from both academy and industry. A wireless sensor node generally has limited storage and computation capabilities, as well as severely constrained power supplies, and the networks often operate in harsh unattended environments. Successful design and deployment of wireless sensor networks thus call for technology advances and integrations in diverse fields including embedded hardware manufacturing and signal processing as well as wireless communications and networking across all layers. We have seen the initial and incremental deployment of real sensor networks in the past decade, for example, the ZebraNet for wildlife tracking, the CitySense for weather and air pollutants reporting, and the Sensormap portal for generic monitoring services, to name but a few; yet the full potentials of such networks in the real world remain to be explored and demonstrated, which involves numerous practical challenges in diverse aspects. This special issue aims to summarize the latest development in the design, implementation, and evaluation of wireless sensor systems. We received a total of XX papers by the deadline of February 2010, and through a rigorous review process, we have selected 12 papers from them. The first paper “Ambient data collection with wireless sensor networks” presents a general survey of the current data collection designs for wireless sensor networks. It is divided into data gathering and message dissemination, which reflects on the many-to-one data collection pattern and the one-to-many control dissemination pattern. We classify the remaining papers into two categories: (1) system implementation, prototyping, and deployment; and (2) individual module/protocol design and optimization.

Metaheuristic algorithms are widely used to address complex real-world optimization problems, but many existing algorithms face challenges such as slow convergence, low accuracy, and susceptibility to local optima. The newly proposed Crested Porcupine Optimizer (CPO), while demonstrating effectiveness across domains, still suffers from these limitations in practical applications, restricting its performance in solving complex optimization tasks like wireless sensor network (WSN) deployment. To address these shortcomings, this study aims to enhance CPO’s overall performance by developing an improved version called the Enhanced Crested Porcupine Optimizer (ECPO). The methodology of ECPO integrates four key enhancement strategies: (1) Sobol sequences for population initialization, ensuring uniform distribution of initial solutions to boost global search capability; (2) a guided search strategy based on the global optimal solution, directing the algorithm toward optimal regions to reduce ineffective exploration; (3) an adaptive Lévy flight search strategy, maintaining population diversity and improving convergence accuracy; and (4) a centroid-based reverse learning strategy for population updates, expanding search space coverage and accelerating convergence. The performance of ECPO was validated on four authoritative benchmark suites (CEC2014, CEC2017, CEC2020, CEC2022) by comparing it with classical algorithms (e.g., PSO, DE), recently proposed algorithms (e.g., BKA, SBOA), and CEC-winning algorithms (e.g., LSHADE, AGSK). Statistical results show ECPO outperformed most comparison algorithms in 93.81%, 93.33%, 75.71%, and 86.90% of tests on the four benchmarks, respectively, exhibiting significant advantages in convergence speed, accuracy, and stability. Additionally, when applied to WSN node deployment optimization, ECPO achieved a higher coverage rate (average 84.95%) and better robustness than competing algorithms, with more rational node distribution and minimal resource waste. These findings confirm that ECPO effectively overcomes the limitations of the original CPO and outperforms many state-of-the-art optimizers. As a high-performance metaheuristic algorithm, ECPO not only excels in numerical optimization but also demonstrates broad applicability in practical engineering problems like WSN deployment, providing a reliable tool for solving complex optimization challenges.

This research highlights the need for more accurate wireless sensor network deployments (WSN) models to support decision-making. Specifically, it presents the effects of terrain variations on network connectivity in Wireless Sensor Networks (WSN) deployments. Through use of statistical hypothesis testing, visualization, and Analysis of Variance (ANOVA), results support significant difference in network connectivity when comparing deployment performance under ideal versus more practical or irregular terrains. These results point to the inadequacy of current simulation environments for supporting deployment decision-making, and point to the need for future research that that improves WSN performance via better deployment techniques.

This paper addresses the issue of Wireless Sensor Network (WSN) deployment. We investigate this problem in the case where the monitored area is characterized by a geographical irregularity of the sensed events. Precisely, we consider that each point of the deployment area requires a minimum threshold guarantee on the event detection probability. Our proposed scalable deployment method, named Potential Field-based Deployment Algorithm (PFDA), is based on the Potential Field and the Virtual Force approaches. Our proposal is able to (1) satisfy the required event detection probability threshold for each point, in a large-scale area, while minimizing the number of deployed sensors and (2) to ensure the network connectivity. The results and evaluation analysis show that PFDA outperforms the other strategies proposed in literature.

Using wireless sensor networks (WSNs) in delay and reliability critical environments is highly desired due to their unique advantages such as low cost, ease of deployment, and redundancy. However, successful deployment of resource-limited WSNs in such applications requires strict quality-of-service (QoS) provisioning techniques to meet the desired latency and reliability targets of those applications. Implementation of QoS techniques in WSNs is significantly challenging since WSNs have been initially devised for low-data-rate non-real-time applications. Therefore, WSN designers and developers resort to different cross-layer interaction and optimization techniques to provision QoS in WSNs. In this paper, we present a survey on the state of the art of cross-layer QoS approaches in wireless terrestrial sensor networks to achieve delay and reliability bounds in critical applications. Our paper provides a unique classification of cross-layer QoS approaches in WSNs that allows surveying a large amount of studies with utmost clarity. Furthermore, we highlight the main challenges of implementing QoS protocols in WSNs and present an overview of QoS-aware WSN applications.

It is a recent trend to consider wireless sensor networks in harsh industrial environments. With actual deployment of wireless sensor networks, it would be desirable to make a concrete deployment plan regarding connectivities and to place sensors by grouping them according to the planned deployment points, even more in case of targeting multiple objects to be sensed and monitored in harsh environments. The connectedness of groups as well as individual sensors is important specifically for real-time data acquisitions and even more if there are no external communication links among these groups. In this paper, we focus on the connectivity of sensor groups, rather than the individual sensors only, and propose a novel <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">group connectivity model</i> so as to analyze group connectivity and to make a concrete deployment plan of sensor groups with regard to the internal distribution of sensors and group positions. We believe that the proposed model should be useful in planning the deployment of wireless sensor networks in harsh industrial environments where running wires is less practical and also prohibitively expensive.

Wireless sensor networks consist of small nodes with sensing, computation and wireless communication capabilities. Various architectures and node deployment strategies have been developed for wireless sensor network, depending upon the requirement of application. Sensor networks are used in different applications e.g. environmental monitoring, habitat monitoring, home automation, military application etc. In this paper we present survey of state-of-the-art of architecture and node deployment in wireless sensor network. We present the characteristics of the environment in which the sensor networks may deploy. Node deployment in wireless sensor network is application dependent and can be either deterministic or randomized. But in both the cases coverage of interested area is the main issue. We also explain the routing protocols for wireless sensor network.

This paper proposes an idea of using heuristic local search procedures specific for single-objective optimisation in multiobjectie evolutionary algorithms (MOEAs). In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) hybridised with a multi-start single-objective metaheuristic called greedy randomised adaptive search procedure (GRASP). In our method a multiobjetive optimisation problem (MOP) is decomposed into a number of single-objecive subproblems and optimised in parallel by using neighbourhood information. The proposed GRASP alternates between subproblems to help them escape local Pareto optimal solutions. Experimental results have demonstrated that MOEA/D with GRASP outperforms the classical MOEA/D algorithm on the multiobjective 0–1 knapsack problem that is commonly used in the literature. It has also demonstrated that the use of greedy genetic crossover can significantly improve the algorithm performance.

In order to solve the deployment problem, which is considered a major issue that faces the design of efficient Wireless Sensor Networks (WSNs), a novel deployment algorithm based on an Enhanced Black Widow Optimization algorithm (EBWO) is proposed. The EBWO algorithm aims to determine the optimal number of sensors and their locations for optimizing both the coverage and the deployment cost. The BWO algorithm is adapted to solve the deployment problem by introducing a set of enhancements, which improves the search capability and the run time of the algorithm. A chaotic initialization is employed in the EBWO algorithm to strengthen the exploration capability of the initial population. Moreover, a modified reproduction mechanism is designed to assist the algorithm in optimizing the number of deployed sensors. Comparisons with modern state-of-the-art deployment methods show that the EBWO algorithm can deliver excellent solutions, where it is ranked first during all the simulations with a coverage difference varying between 3.34% and 7.94% from the other competitors.

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

Distributed key management is a major challenge in the design and deployment of secure wireless sensor networks (WSNs). In most existing distributed key management schemes, a common assumption is that all sensor nodes have the same capability. However, recent studies have demonstrated that heterogeneities of the sensor nodes, in particular the heterogeneities of energy capacity and transmission capability, can significantly improve the connectivity and lifetime of the WSNs. Consequently, it is important to design key management schemes for heterogeneous WSN (HWSN). In this paper, we provide a distributed key management scheme for HWSNs. The main contribution of this paper is to develop analytical models for evaluating the performance of the proposed scheme in terms of connectivity, reliability, and resilience. Extensive simulation results show that, with a small number of heterogeneous nodes, the WSN can achieve higher key connectivity, better reliability, and higher resilience. The results also show that our analytical models can accurately predict the performance under a variety of conditions

In this paper, the influence of correlated data from different sensors on the location of the sensors and the consumed energy of wireless sensor network are introduced, and the performance of a joint entropy encoding strategy which compresses the correlated data along routing is analyzed. During transmission, relay sensors jointly encode their own sampled data and the data received from upward sensors. The consumed energy of the network is minimized through optimally choosing the location of sensors. For the sake of prolonging network lifetime, a scheme to balance the consumed energy between different sensors is proposed, which combines the correlation between data and the amounts of data transmitted by different sensors, to make the transmitted energy consumed by different sensors tend to be uniform by adjusting the distance between sensors. The applications of the schemes in one-dimensional wireless sensor network are presented and the numerical results show that these schemes have excellent performance.

The advancement of smart city infrastructure depends heavily on the deployment of wireless sensor networks (WSNs) to monitor environmental conditions, traffic, healthcare systems, and public safety in real time. However, the limited energy resources of sensor nodes pose a critical challenge to network longevity, data reliability, and service continuity. This research presents the design and evaluation of an adaptive energy-efficient routing protocol that dynamically integrates residual energy levels, spatial topology awareness, and traffic load adaptability into routing and cluster-head selection processes. Using simulation-based experiments, the proposed protocol was compared with benchmark schemes including LEACH, TEEN, and SEP under varying traffic and deployment conditions. Results reveal that the proposed approach significantly delays first-node-death, increases overall network stability, and sustains higher Packet Delivery Ratio (PDR)s across different traffic loads, while simultaneously improving throughput and reducing end-to-end delay. Statistical analysis confirms that these improvements are systematic and not incidental, validating the hypothesis that energy-efficient routing in WSNs requires a holistic and adaptive design. The findings emphasize the potential of such protocols in enabling cost-effective, reliable, and scalable WSN deployments for smart cities. Practical recommendations derived from this research highlight the importance of incorporating adaptive cluster-head selection, multipath routing, and traffic-awareness into network design, while encouraging pilot projects and integration with renewable energy and edge computing to further optimize performance. Collectively, the study provides a pathway for transforming WSNs into robust, energy-aware communication backbones that underpin sustainable urban intelligence systems.