Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography

Efficient deployment of Wireless Sensor Networks (WSNs) is critical to ensuring optimal performance across key metrics such as coverage, connectivity, energy efficiency, and network longevity. Traditional deployment strategies such as random, grid-based, and deterministic placements often fail to accommodate heterogeneous node capabilities or adapt to dynamic environmental conditions. This study proposes a Particle Swarm Optimization (PSO)-based deployment framework tailored for heterogeneous WSNs. The framework incorporates intelligent role assignment, metaheuristic optimization, and adaptive maintenance phases to balance coverage quality, minimize energy consumption, and enhance fault tolerance. A comprehensive simulation-based evaluation involving nine deployment strategies demonstrates that the proposed PSO-based approach achieves superior performance, including an average coverage of 91.4 ± 1.8%, extended operational lifetime exceeding 3,400 rounds, and improved connectivity robustness. The framework also exhibits strong scalability and resilience in the presence of node failures. These findings establish the proposed deployment model as a viable solution for real-world, large-scale, and energy-constrained WSN applications.

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

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.

Large-scale wireless sensor network (WSN) deployment is a major challenge for Internet of Things (IoT) to connect physical world through sensors or tags at the scale of billions of objects. Today's WSNs normally use dedicated gateways to bridge sensors and IP networks. The installation and maintenance of such a WSN infrastructure are expensive and non-scalable. As the number of smart phone users grows exponentially every year, a powerful mobile computing platform could be used as an alternative WSN infrastructure, which is ubiquitous and scalable. This paper studies the major challenges of using mobile phones as spontaneous gateways of WSNs in IoT systems. The challenges include (1) matching the throughput of mobile phone gateways and sensor data rate at hotspot locations, (2) securing sensor data access and (3) providing accounting records of gateway the service offered by mobile phones. In this paper, we show that using a name-based future Internet architecture (FIA), such as Mobility First, these challenges can be overcome effectively though its native networking layer functions. A proof-of-concept prototype system demonstrates the feasibility of proposed solution. The system delivers a temperature sensor data from an Android phone directly to multiple applications via in-network multicast over an Mobility First network test bed.

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.

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.

Deployment plays an essential role in determining the future performance of any Wireless Sensor Network (WSN). Random deployment is a common method to scatter Sensor Nodes (SNs) within a vast and unreachable region. In this paper we have proposed a Centrifugal Cannon based Sprinkler (CCS) to scatter SNs from helicopter moving over the candidate region. The proposed model is time efficient and effective as it minimizes the number of scans over the region.

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.

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.

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

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.

Wireless Sensor Network (WSN) is the main element of modern Industrial Internet of Things system in event monitoring. However, WSN is unable to respond for longer life due to the limited battery backup. So, it is hard to monitor the event of interest for longer span of time with deployed WSN. This can be resolved by utilizing energy efficient design approach for data communication. Cluster based routing scheme could be the most prominent design approach for the said option. Nevertheless, on the cost of certain design constraints. Although, most of the real-life applications are designed based on clustering scheme. But, it utilizes random node deployment strategy to support assumptions of prethrown nodes in test field. Similarly, uniform node deployment is also being demanded in some specific applications. Thus, there is a high need to design and implement clustering protocol that can work satisfactorily independent of the node deployment. This paper presents an innovative design approach with Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network. In proposed approach cluster head (CH) is selected based on index modeling with available node attributes. Proposed design ensures selected CH offers better node degree and intra-connectivity inside the network. Here, we try to minimize the efforts of CH and cluster member such that energy efficiency of each individual node get extend to a high level. Hence, it improves stability period, lifetime and throughput of the network. Simulation of proposed approach outperforms the existing LEACH, SEP and DEEC by more than 1.3 times.

Wireless Sensor Network (WSN) is a spatially distributed network. It contains many numbers of distributed, self-directed, small, battery powered devices called sensor nodes or motes. In recent years the deployment of WSN in various application domains are growing in a rapid pace as with the upcoming boom of Internet of Things (IoT) and Internet of Everything (IoE). However, the effectiveness of the WSN deployment is restricted due to the constrained computation and power source. Hence, many researchers have been proposing new approaches and models to improve the efficiency of the domain specific WSN deployment procedures. Though, many research communities addressing various issues in WSN deployment, still the privacy and security of such networks are susceptible to various network attacks. Thus, it is necessary to practice different models for authentication and privacy preservation in a highly dynamic resource constrained WSN environment to realize the effectiveness and efficiency of the deployment. Hence, this paper addressing an authentication scheme that can reduce energy consumption without compromising on security and privacy. In order to provide a light weight authentication mechanism, this paper proposing an authentication mechanism for WSN deployment by combining the features of Elliptic Curve Cryptography (ECC) and Hexagonal numbers. The feature of ECC is used to reduce the key size and the effectiveness of generating hexagonal numbers is used for minimizing the energy consumption in a resource constrained WSN environment. The results of the proposed approach are evaluated with the different authentication models and the results were indicating that the proposed approach can perform better than the other approaches.

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.

Sensing deployment is an important issue addressed in wireless sensor network (WSN). This paper proposes a virtual force directed co-evolutionary particle swarm optimization (VFCPSO) algorithm, which uses a combined objective function to achieve the tradeoff of coverage and energy consumption. By considering deployment as an optimization problem, VFCPSO is more reliable and flexible for WSN, since it can satisfy the combined requirements instead of only enlarging coverage. Because centralized VFCPSO is computation-expensive for WSN, a distributed VFCPSO (D-VFCPSO) is proposed by achieving optimization with the collaboration of multiple sensor nodes. Simulation results show that the D-VFCPSO has the better performance than VF-style algorithms and centralized VFCPSO in the terms of time cost and deployment results. It is obvious that the proposed D-VFCPSO can rapidly and effectively achieve the sensing deployment in WSN.