An Enhanced Virtual Force Algorithm for Diverse k-Coverage Deployment of 3D Underwater Wireless Sensor Networks

Based on the principles of wireless mesh network (WMN), clustering of wireless sensor network (WSN) was reconstructed by combination of WMN and WSN into a topology named wireless mesh sensor network (WMSN). In terms of Voronoi diagram and Delaunay triangulations of computational geometry, central degree theory of complex network and heuristic algorithm based on mean weight of communication energy, a optimal deployment algorithm named WHD algorithm which can figure out large scale WMSN was designed. The study results show that the combination of WSN and WMN can achieve mesh wide hybrid network topology; WHD algorithm can be an effective solution to optimal deployment of large-scale WMSN. The experimental results show that WHD algorithm not only ensures optimal deployment of large-scale WMSN but also has the energy saved features, and then extends the network life-cycle efficiently.

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UW...

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.

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

In the past few decades, optical communication with high data rates up to Gb/s in underwater wireless sensor networks (UWSN) has been extensively investigated. However, now a days researchers are highly focused on proposing/implementing localization algorithms for underwater optical wireless sensor networks (UOWSN) that are both fast in computation and cost efficient in terms of hardware design. In this paper we have used two different setups, one consisting of one led as a transmission node Tx. Where we exploit the effects of different angle on optical communication by implementing Lambert cosine law in a (UOWSN). The second setup is made of a led clutch which can communicate in 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> . Both setups are used to estimate the location of a sensor node Rx in (UOWSN). To complete the task of localization estimation, a multilateration algorithm based on Euclidean distance between the Tx and Rx nodes is used, where as Gauss–Newton method was used to find the minimum of the function.

Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues of the distributed Kalman filtering algorithm. In what follows, under the condition of MNUO, conditions to ensure its stability are given and the results about the relation of the filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.

The penetration of Wireless Sensor Networks (WSNs) in various applications poses a high demand on reliable data storage, especially considering sensor networks are usually deployed in harsh environment. In this paper, we introduce Heterogeneous Wireless Sensor Networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques, in order to improve the reliability of data storage. Taking into account the cost of both data delivery and storage, we propose an algorithm to jointly optimize data routing and storage node deployment. This problem is a binary non-linear combinatorial optimization, and it is highly non-trivial to design efficient algorithms due to its NP-hardness. By levering the Markov approximation framework, we elaborately deign a Continuous Time Markov Chain (CTMC) based scheduling algorithm to drive the storage node deployment and the corresponding routing strategy. Extensive simulations are performed to verify the efficacy of our algorithm.

The optimization for deployment of wireless sensor networks is helpful to improve the sensor coverage and the reliability of system. In this work, an enhanced fireworks algorithm is applied to the dynamic deployment of mobile sensor networks. Compared with the existing mainstream swarm intelligence algorithms, this proposed approach has the advantages of good convergence rate, fast convergence and better coverage, which is closer to the theoretical coverage value. Experimental results show that the enhanced fireworks algorithm is able to get the optimal deployment quickly and effectively in the actual scene.

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.

When detecting a target or monitoring a physical phenomenon in a region, the deployment problem is fundamental in these applications. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. It is well known that the mobility of sensor nodes can improve the coverage and the probability of the detecting, so we deal with the problem of detecting a target using hybrid sensor networks which contain both stationary sensors and mobile sensors. In this paper, to begin with, we prove that the node deployment problem is NP-complete. Then, one polynomial algorithm for node deployment in hybrid wireless sensor networks is proposed, which aims at minimizing the number of all sensors to reduce the cost. The simulations verify the efficiency of our algorithm.

Routing protocols based on protocol operations for underwater wireless sensor network: A survey

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.

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

Wireless sensor networks (WSNs) are network of physical small sensors which are interconnected through wireless links. WSNs are ubiquitous in diverse types of environment such as air interface and underwater acoustic communication. WSNs are responsible for collecting data, accomplishing appropriate operations on it and if required then relay it to the sink node. WSNs had many issues and challenges specifically in underwater wireless sensor network (UWSN). Secondly there is an issue of network life time as the sensor nodes have limited battery and it is challenging to replace it frequently specially in UWSN. An important challenge in the performance of the DBR is the energy constraint due to limited battery life time, which can cause the entire network to go down. In this paper, we propose an Energy Harvesting Depth Based Routing Protocol (EH-DBR) protocol. Our proposed protocol (EH-DBR) sensor node harvest and store power via external acoustic source in the form of packets. The key advantage of our protocol is that its lifetime will be extended indefinitely. Moreover, our routing protocol can take advantage of DBR architecture without introducing extra cost. We conduct extensive simulations. The results show that EH-DBR can achieve very high packet delivery ratios due to long time performance.

Source localization utilizing wireless sensor networks (WSNs) is getting more popular in marine applications because the global positioning system is impractical underwater. Underwater WSNs (UWSNs) necessitate underwater acoustic (UWA) signaling, as opposed to terrestrial WSNs (TWSNs), RSSI-based source localization i.e., Received Signal Strength Indicator is explored in this article. In this paper, we provide an Aqua-Sim simulator, an underwater sensor network simulator built on the NS-3 protocol. Underwater Acoustic Sensor Network and Shadowing approaches are proposed and also a new set of Acoustic data is generated by Shadowing using mathematical calculations. In UWSN, the weighted centroid method is used. We chose RSSI to calculate locations. We were able to improve the accuracy and localization error numbers and mathematical formulae even more by using the modeling approach. Another unique aspect of our approach is that we only employed four anchoring apexes to analyze localization and find the position of the sensor nodes.