Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks

Existing work on placing additional relay nodes in wireless sensor networks to improve network connectivity typically assumes homogeneous wireless sensor nodes with an identical transmission radius. In contrast, this paper addresses the problem of deploying relay nodes to provide fault-tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. Depending on the level of desired fault-tolerance, such problems can be categorized as: (1) full fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths between every pair of sensor and/or relay nodes; (2) partial fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths only between every pair of sensor nodes. Due to the different transmission radii of sensor nodes, these problems are further complicated by the existence of two different kinds of communication paths in heterogeneous wireless sensor networks, namely two-way paths, along which wireless communications exist in both directions; and one-way paths, along which wireless communications exist in only one direction. Assuming that sensor nodes have different transmission radii, while relay nodes use the same transmission radius, this paper comprehensively analyzes the range of problems introduced by the different levels of fault-tolerance (full or partial) coupled with the different types of path (one-way or two-way). Since each of these problems is NP-hard, we develop O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-approximation algorithms for both one-way and two-way partial fault-tolerance relay node placement, as well as O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> )-approximation algorithms for both one-way and two-way full fault-tolerance relay node placement (sigma is the best performance ratio of existing approximation algorithms for finding a minimum k-vertex connected spanning graph). To facilitate the applications in higher dimensions, we also extend these algorithms and derive their performance ratios in d-dimensional heterogeneous wireless sensor networks (d ges 3). Finally, heuristic implementations of these algorithms are evaluated via simulations.

A wireless sensor network consists of many low-cost, low-power sensor nodes, which can perform sensing, simple computation, and transmission of sensed information. Long distance transmission by sensor nodes is not energy efficient since energy consumption is a superlinear function of the transmission distance. One approach to prolonging network lifetime while preserving network connectivity is to deploy a small number of costly, but more powerful, relay nodes whose main task is communication with other sensor or relay nodes. In this paper, we assume that sensor nodes have communication range r>0, while relay nodes have communication range Rgesr, and we study two versions of relay node placement problems. In the first version, we want to deploy the minimum number of relay nodes so that, between each pair of sensor nodes, there is a connecting path consisting of relay and/or sensor nodes. In the second version, we want to deploy the minimum number of relay nodes so that, between each pair of sensor nodes, there is a connecting path consisting solely of relay nodes. We present a polynomial time 7-approximation algorithm for the first problem and a polynomial time (5+epsi)-approximation algorithm for the second problem, where epsi>0 can be any given constant

The relay node placement problem in wireless sensor network (WSN) aims at deploying the minimum number of relay nodes over the network so that each sensor can communicate with at least one relay node. When the deployed relay nodes are homogeneous and their communication ranges are circular, one way to solve the WSN relay node placement problem is to solve the minimum geometric disk cover (MGDC) problem first and place the relay nodes at the centers of the covering disks and then, if necessary, deploy additional relay nodes to meet the connection requirement of relay nodes. It is known that the MGDC problem is NP-complete. A novel linear time approximation algorithm for the MGDC problem is proposed, which identifies covering disks using the regular hexagon tessellation of the plane with bounded area. The approximation ratio of the proposed algorithm is (5+ϵ), where0&lt;ϵ≤15. Experimental results show that the worst case is rare, and on average the proposed algorithm uses less than 1.7 times the optimal disks of the MGDC problem. In cases where quick deployment is necessary, this study provides a fast 7-approximation algorithm which uses on average less than twice the optimal number of relay nodes in the simulation.

Due to adverse effects of hostile deployment environment(s), sensors nodes (SNs) deployed in the network may drain or get damaged. This attributes for loss of connectivity between the sensors and the sink node. Hence it is necessary to design the wireless sensor network (WSN) in a manner that the network is capable of coping up with the failure of few nodes/links. Using single hop data communication model reduces the lifetime of the network as the SNs are resource constrained (in terms of battery). Relay nodes (RNs) facilitate in improving network lifetime, fault tolerance and/or connectivity. Finding minimum number of relay nodes for a fault tolerant, fully connected network is a NP Hard problem. In this paper, we follow two phase relay node placement procedure to achieve the objective. In the first phase, we cluster the SNs using mean shift algorithm and place RNs as the cluster heads. In the second phase, we use metaheuristic algorithms like Moth Flame Optimization (MFO), Differential Evolution (DE), Bat algorithm (BA) and Biogeography Based Optimization algorithm (BBO) to place the RNs such that the required fault tolerance is achieved along with fully connected network. Extensive simulation results prove that MFO algorithm performs better for fault tolerant relay node placement problem.

Two fundamental functions of the sensor nodes in a wireless sensor network are to sense its environment and to transmit sensed information to a basestation. One approach to prolong sensor network lifetime is to deploy some relay nodes whose main function is to communicate with the sensor nodes, other relay nodes, and the basestations. It is desirable to deploy a minimum number of relay nodes to achieve certain connectivity requirement. In this paper, we study four related fault-tolerant relay node placement problems, each of which has been previously studied only in some restricted form. For each of them, we discuss its computational complexity and present a polynomial time O(1)-approximation algorithm with a small approximation ratio. When the problem reduces to a previously studied form, our algorithm either improves the previous best algorithm or reduces to the previous best algorithm.

In 2tWSN(two-tiered wireless sensor network), relay nodes are responsible for delivering the sensed data from sensor nodes to base stations. From the perspective of business, it needs more relay nodes to ensure network connectivity. From the perspective of economy, relay nodes are relatively expensive, so people are intended to place a minimum number of relay nodes while guaranteeing the data collecting and delivering. In this paper, we firstly model fault tolerant RNP(relay node placement) problem in 2tWSN as a graphic problem, denoted by DBY-HCG. It distinguishes relay nodes from base stations. Secondly, we figure out keCi-RNPB algorithm. Moreover, an approximation algorithm aiming at finding the minimum length of k-vertex disjoint paths in connected sub-graph is raised up to support our algorithm's extensibility. Our algorithm is also the first solution for constrained version of fault tolerant relay node placement problem in 2tWSN. Extensive experiments have been executed in both unconstrained and constrained situations and the numeric results show that our solution is close to optimal solution.

For energy-constrained wireless sensor networks (WSNs), the topology control issue is fundamental and important to maximize the overall network lifetime. Recently a two-tiered network model has been proposed for this purpose. It is a cluster-based network model composed of application and sensor nodes. In this model the application nodes are easy to run out of energy because they not only serve as cluster heads for aggregating data packets from the sensor nodes in their clusters but also are responsible for forming a connected network topology for information dissemination at the higher tier. In order to balance nodes' power consumption and extend the topological lifetime of a WSN with regard to a certain amount of initial energy provision, we propose a logical three-tiered network model which consist of relay nodes (RNs), application nodes (ANs) and sensor nodes (SNs). Under this architecture, the goal of lower-tier SNs is to sense the playing fields and middle-tier ANs only need to gather data from their clusters; Upper-tier RNs are designed to route information as efficiently as possible. In this paper, we first formulate the three-tiered WSN's model as well as the power consumption model of WSNs. Then considering one dimension relaying, we figure out the RNs' optimal placement for each AN which can considerably minimize the total power consumption for WSNs. Finally, we give some criteria for two-dimension relaying.

Connected dominating set (CDS) problem in unit disk graph has a significant impact on an efficient design of routing protocols in wireless sensor networks. In this paper, an algorithm is proposed for finding minimum connected dominating set (MCDS) using dominating set. Dominating sets are connected by using Steiner tree. The algorithm goes through three phases. In first phase dominating set is found, in second phase connectors are identified, connected through Steiner tree. In third phase the CDS obtained in second phase is pruned to obtain a MCDS. MCDS so constructed needs to adapt to the continual topology changes due to deactivation of a node due to exhaustion of battery power. These topological changes due to power constraints are taken care by a local repair algorithm that reconstructs the MCDS using only neighborhood information. Simulation results indicate both the heuristics are very efficient and result in near optimal MCDS.

A shortest path tree based algorithm for relay placement in a wireless sensor network and its performance analysis

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.

Clustering is essential in Wireless Sensor Networks (WSNs) as it facilitates less energy consumption routing, network partitioning, and the optimization of routing pathways depending upon the parameters such as energy consumption and distance. Numerous Clustering Routing Algorithms (CRAs) have been formulated for a long time. Among them, a Relay-optimized Dual Mode energy-minimized SCA-Levy (RDMSCA-Levy)-based CRA can choose the optimal Relay Node (RN) in multi-hop WSNs to enhance inter- and intra-cluster transmission. However, the major limitation of the static node deployment in WSN is the possibility of energy holes’ formation, even with high residual energy. Therefore, this article proposes an Enhanced RDMSCA-Levy (ERDMSCA-Levy) algorithm for multi-hop WSNs to detect and prevent energy holes. It incorporates the Voronoi geometry-based hole detection and relay positioning model-based hole prevention approaches. In the hole detection phase, the network region is split into Voronoi cells, where each sensor node serves as a generating point in the geometry. Then, the distance between the sensor nodes within the Region of Interest (ROI) and the edge points of their corresponding Voronoi cells Is determined. Any abnormal distances are detected as potential holes in the network. After that, a relay-based hole prevention is introduced, which involves repositioning RNs within the network based on the energy and distance. The updated locations of these RNs are then integrated into the Deep QLearning (DQL) algorithm to choose the best RN position and optimize the coverage. Finally, the findings reveal that the ERDMSCA-Levy attained a better efficiency when evaluated with the existing CRAs.

In this paper, we conduct theoretical analysis and numerical simulation on the sensor network spectrum detection technology based on Internet of Things. In the field of Internet of things, information service system as a means of information, intellectualized, is an important research direction. In structure, Internet information service system is mainly composed of persistent database. Wireless sensor network foot a composed of a set of information collection, processing, and wireless transmission in the integration of wireless self-organizing network composed of micro sensor nodes. Sensor nodes structure will be designed according to different application scenarios, the structure of the wireless sensor network node is used to describe the different periods and different types of sensor nodes. If the realization of cognitive radio technology in wireless sensor network, not only can alleviate the congestion of public frequency band, and reduce the work frequency of the wireless sensor network also makes the node after single jump coverage is wide and change.

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.

With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a Minimum Connected Dominating Set (MCDS) problem. However, since MCDS is NP-complete on general graphs, existing heuristic and exact algorithms suffer from limited coverage, poor connectivity, and high computational cost. To address these issues, we propose TGN-MCDS, a novel algorithm built upon the Temporal Graph Network (TGN) architecture, which leverages graph neural networks for cluster head selection and efficiently learns time-varying network topologies. The algorithm adopts a multi-objective loss function incorporating coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy to guide model training. Simulation results demonstrate that TGN-MCDS rapidly achieves near-optimal CH sets with full node coverage and strong connectivity. Compared with Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB) methods, TGN-MCDS produces fewer and more stable CHs, significantly improving cluster stability while maintaining high computational efficiency for real-time operations in large-scale FANETs.