Minimum Number Of Sensor Nodes Research Articles

Enhanced DV-Hop Algorithm for Efficiency Energy and Network Quality in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are wireless networks with multiple sensor nodes covering a relatively large area. One of the weaknesses of WSNs is the relatively high energy consumption, which affects the network quality of service. Although WSN network routing using the DV-Hop algorithm is widely used because of its simplicity, improvements need to be made to improve energy efficiency, which impacts network lifetime. This article proposes a method to enhance energy efficiency by comparing the original DV-Hop algorithm with the enhanced DV-Hop algorithm. There are three approaches to enhancing the DV-Hop algorithm. Firstly, the CH node selection is based on the distance to the Base Station so that the selected CH node does not have a far distance from the base station. Secondly, the CH node selection must have a minimum number of sensor nodes as neighbors. Lastly, each selected CH node calculates the minimum distance to the previously selected CH node to ensure that the selected CH node is not in a nearby position. The proposed approach obtains better data packet transfer rate, energy efficiency, and network lifetime results using Matlab software simulation that compares the enhanced DV-Hop algorithm with the original DV-Hop algorithm and three other routing algorithms.

Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

Target coverage and continuous connection are the major recital factors for Wireless Sensor Network (WSN). Several previous research works studied various algorithms for target coverage difficulties; however they lacked to focus on improving the network’s life time in terms of energy. This research work mainly focuses on target coverage and area coverage problem in a heterogeneous WSN with increased network lifetime. The dynamic behavior of the target nodes is unpredictable, because the target nodes may move at any time in any direction of the network. Thus, target coverage becomes a major problem in WSN and its applications. To solve the issue, this research work is motivated to design and develop an intelligent model named Distributed Flexible Wheel Chain (DFWC) model for efficient target coverage and area coverage in WSN applications. More number of target nodes is covered by minimum number of sensor nodes that can improve energy efficiency. To be specific, DFWC motivated at obtaining lesser connected target coverage, where every target is available in the monitoring area is covered by a smaller number of sensor nodes. The simulation results show that the proposed DFWC model outperforms the existing models with improved performance.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

A Novel Coverage Improved Deployment Strategy for Wireless Sensor Network

Coverage of the bounded region gets importance in Wireless Sensor Network (WSN). Area coverage is based on effective surface coverage with a minimum number of sensor nodes. Most of the researchers contemplate the coverage region of interest as a square and manifest the radio ranges as a circle. The area of a circle is much higher than the area of a square because of the perimeter. To utilize the advantage of the circle, the coverage region of interest is presumed as a circle for sensor node deployment. This paper proposes a novel coverage improved disc shape deployment strategy. Comparative analysis has been observed between circle and square regions of interest based on the cumulative number of sensor nodes required to cover the entire region. A new strategy named as disc shape deployment strategy is also proposed. Traditional hexagon and strip-based deployment strategies are compared with the disc shape deployment strategy. The simulation result shows that the circle shape coverage region of interest extremely reduces the required number of sensor nodes. The proposed deployment strategy provides desirable coverage, and it requires few more sensor nodes than hexagon shape deployment strategy.

Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

SummaryCoverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

Discrimination of attractors with noisy nodes in Boolean networks

Observing the internal state of the whole system using a small number of sensor nodes is important in analysis of complex networks. Here, we study the problem of determining the minimum number of sensor nodes to discriminate attractors under the assumption that each attractor has at most K noisy nodes. We present exact and approximation algorithms for this minimization problem. The effectiveness of the algorithms is also demonstrated by computational experiments using both synthetic data and realistic biological data.

Minimal Number of Sensor Nodes for Distributed Kalman Filtering

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.

Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks.

In the wireless sensor network, the lifetime of the network can be prolonged by improving the efficiency of limited energy. Existing works achieve better energy utilization, either through node scheduling or routing optimization. In this paper, an efficient solution combining node scheduling with routing protocol optimization is proposed in order to improve the network lifetime. Firstly, to avoid the redundant coverage, a node scheduling scheme that is based on a genetic algorithm is proposed to find the minimum number of sensor nodes to monitor all target points. Subsequently, the algorithm prolongs the lifetime of the network through choosing redundant sleep nodes to replace the dead node. Based on the obtained minimum coverage set, a new routing protocol, named Improved-Distributed Energy-Efficient Clustering (I-DEEC), is proposed. When considering the energy and the distance of the sensor node to the sink, a new policy choosing the cluster head is proposed. To make the energy load more balanced, uneven clusters are constructed. Meanwhile, the data communication way of sensor nodes around the sink is also optimized. The simulation results show that the proposed sensor node scheduling algorithm can reduce the number of redundant sensor nodes, while the I-DEEC routing protocol can improve the energy efficiency of data transmission. The lifetime of the network is greatly extended.

Energy-efficient coverage and connectivity of wireless sensor network in the framework of hybrid sensor and vehicular network

Hybrid Sensor and Vehicular Network (HSVN) merges both Vehicular Ad hoc Network (VANET) and Wireless Sensor Network (WSN) to create a framework of collaboration between the two networks. The main objective of this collaboration is to provide real-time detection of dangerous road conditions for the improvement of road safety. To create a reliable HSVN, coverage and connectivity problems of WSN represent the most fundamental issues to ensure an effective road sensing and a robust data communication in HSVN applications. In this respect, we have proposed a distributed protocol for ensuring both coverage and connectivity of WSN in the framework of HSVN. The proposed protocol ensures the detection of any event occurring on the road and the connectivity between sensor nodes even in the case of sensor node failure. Also, the energy consumption is reduced by keeping only a minimum number of sensor nodes in active state. The WSN lifetime is extended by balancing the energy consumption among all sensor nodes and by allowing the sensor nodes to serve alternatively as a gateway which is responsible for reporting any detected event to the passing vehicles. Simulation results show that the proposed protocol achieves 100% of road coverage and energy consumption is improved up to 14%. The proposed protocol is reliable in the case of sensor node failure; coverage and connectivity are improved up to 13% and 87%, respectively, in the case of 30% of failed sensor nodes. These results indicate that the proposed protocol is promising as it outperforms the existing ones.

Analytic evaluation of non-uniformities for coverage probability computation of randomly deployed wireless sensor network

The border region of a country is almost a remote hostile geographic region which requires continuous surveillance to prevent from unauthorised intrusion. Sensor nodes can be deployed randomly in such regions for surveillance. The quality of surveillance is measured by coverage rate. This application imposes many non-uniformities for coverage rate computation such as asymmetrical locations, irregular sensing range and biased coverage due to coverage redundancy and coverage holes. This paper proposes a coverage probability computation (CPC) protocol that considers these non-uniformities while computing coverage rate. CPC computes the distribution of coverage probabilities within the sensing region of each sensor node using its probabilistic sensing range. This paper also derives the lower bound for a minimum number of sensor nodes required to cover the hostile region. The simulation results of CPC show that at an optimum density of sensor nodes the hostile region can be covered up to a threshold level.

Attempting to Model a Fresh Three Dimensional Coverage Scheme for Wireless Sensor Networks

The coverage framework and the installation policies are straightforwardly linked to the feasible distribution of the restricted resources for the wireless sensor network like node energy, transmission bandwidth and energy for estimation along with the quality enhancement which is immensely resolved for the wireless sensor networks. A three-dimensional coverage model and installation scheme are designed which initially performs an assessment of the usual polyhedron frameworks in the three-dimensional prospects, a coverage model based on cuboids is proposed. The association among the coverage and sensor nodes is assumed along with the minimal number of sensor nodes to preserve the network regions comprehensive coverage is estimated. Lastly, the sensor nodes are installed based on the coverage model after the observation region which is subclassified into restricted three-dimensional grids. The results of analysis reveal that the evaluation against the conventional arbitrary schemes, the sensor number of sensor nodes are minimized efficiently based on the precise ratio of coverage employing the coverage framework and deterministic installation methods.

Maximum Lifetime Target Coverage in Wireless Sensor Networks

The challenge in the deployment of wireless sensor networks is to ensure the coverage of targets with high energy efficiency, particularly when coverage and energy constraints must be taken into account. Many algorithms have been suggested over the years to enhance network lifetime as sensor nodes are powered by batteries. These algorithms divide a set of sensor nodes into a number of subsets which can monitor all targets. The subsets are either joint or disjoint. In this paper, we propose an efficient method to compute the maximum lifetime target coverage. Our proposed method is centred around a target that is covered by a minimum number of sensor nodes. Based on this approach, we also propose an algorithm to expand the network lifetime which guarantees the monitoring of all targets by forming subsets of sensor nodes. Our obtained results are compared with the work reported in Cardei et al. (In: INFOCOM 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE, 3:1976–1984, 2005). They are also compared with a method of computing the maximum lifetime target coverage. The results obtained by simulation show that the proposed algorithm enhances the network lifetime, which illustrates the efficiency of this algorithm.

Target coverage computation protocols in wireless sensor networks: a comprehensive review

Wireless sensor network comprises tiny sensor nodes having the ability of sensing to surveil an area of interest. The amount of area sensed by a sensor node is known as coverage. Coverage is a multi-dimensional quality of service (QoS) metric in terms of diversity in its computation algorithms according to the real-time applications. Different applications require a distinctive level of coverage. But every coverage computation algorithm has one common objective, i.e. to maximize the network’s lifespan and coverage quality with minimum number of sensor nodes. Several algorithms have derived optimal or close-to-optimal solutions based on similar or distinct parameters. This paper presents a concise review of various target coverage protocols for both static and mobile targets. Total target coverage provided by the deployed sensor nodes is a measure of sensing quality. Target coverage is bifurcated as static coverage and dynamic coverage. This paper highlights and describes various application specific target coverage protocols and their QoS metrics and parameters. Each protocol has solved multi-dimensional coverage issues and challenges and proposed optimum solutions. The comprehensive review of the target coverage protocol helps in understanding about various research challenges and its existed solutions in target coverage, detection and tracking in a better way.

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

The combination of Wireless Sensor Networks (WSNs) and edge computing not only enhances their capabilities, but also motivates a series of new applications. As a typical application, 3D Underwater Wireless Sensor Networks (UWSNs) have become a hot research issue. However, the coverage of underwater sensor networks problem must be solved, for it has a great significance for the network’s capacity for information acquisition and environment perception, as well as its survivability. In this paper, we firstly study the minimal number of sensor nodes needed to build a diverse k-coverage sensor network. We then propose a k-Equivalent Radius enhanced Virtual Force Algorithm (called k-ERVFA) to achieve an uneven regional coverage optimization for different k-coverage requirements. Theoretical analysis and simulation experiments are carried out to demonstrate the effectiveness of our proposed algorithm. The detailed performance comparisons show that k-ERVFA acquires a better coverage rate in high k-coverage sub-regions, thus achieving a desirable diverse k-coverage deployment. Finally, we perform sensitivity analysis of the simulation parameters and extend k-ERVFA to special cases such as sensor-sparse regions and time-variant situations.

Investigation of Parameter Effects on Virtual-Spring-Force Algorithm for Wireless-Sensor-Network Applications.

Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN.

Sensing coverage hole identification and coverage hole healing methods for wireless sensor networks

Wireless sensor networks are often deployed for event detection and environmental monitoring. However, their success in providing quality of service can only be ensured if the network does not have any sensing coverage holes. The existence of sensing coverage holes is unavoidable due to various factors such as environmental disasters, random deployment and hardware failure of the sensor nodes. Therefore, detecting the sensing coverage holes is essential for the successful operation of the network. We present a chord-based hole detection method for identifying the sensing coverage holes; this method is effective in identifying both closed and open holes in the region of interest. Since it is also necessary to heal the sensing coverage holes to improve the quality of service of the sensor network, we also propose a sensing coverage hole healing method, namely, the chord-based hole covering (CBHC) method. The CBHC method provides complete sensing coverage of the network using the minimum number of sensor nodes by minimizing the sensing coverage area overlap. Additionally, our proposed method for sensing coverage hole identification can also identify the boundary of the sensor network. The simulation results demonstrate the satisfactory performances of both the proposed hole identification and hole healing methods in identifying the sensing coverage holes and efficiently covering the sensing coverage holes, respectively.

Nodes Deployment for Coverage in Rechargeable Wireless Sensor Networks

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution.

An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications.

How to effectively deploy all wireless sensors and save a system’s energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN.

Target Coverage-Aware Clustering in Directional Sensor Networks: A Distributed Approach

Existing clustering algorithms for directional sensor networks mainly focused on maximizing network lifetime and/or data delivery performances. A distributed clustering algorithm named TRACE considering target coverage is proposed in this paper that improves the clustering performances, as well as, the sensing coverage in the network. TRACE is designed to operate in a fully distributed manner. It is also a light-weight clustering algorithm based on coverage and connectivity, where only single-hop neighborhood information is exploited by the nodes to determine cluster heads (CHs) and two-hop neighborhood messages for gateways. Moreover, a target-coverage algorithm is proposed, where the TRACE CHs try to activate the minimum number of sensor nodes in the network, as well as, greedily maximize the number of covered targets. To measure the performances, the TRACE system is implemented in Network Simulator version 3 (NS-3) and the simulation outcomes depict that the proposed TRACE performs better than the state-of-the-art work in terms of cluster-coverage ratio, network lifetime, and overhead.

Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: an improved genetic algorithm based approach

Energy efficient scheduling of sensor nodes is one of the most efficient techniques to extend the lifetime of the wireless sensor networks (WSNs). Instead of activating all the deployed sensor nodes, a set of sensor nodes are activated or scheduled to monitor the targeted region. While scheduling with lesser number of sensor nodes, coverage and connectivity of the network should be taken care due to the limited sensing and communication range of the sensor nodes. In this paper, we have proposed an improved genetic algorithm (GA) based scheduling for WSNs. An efficient chromosome representation is given and it is shown to generate valid chromosome after crossover and mutation operation. The fitness function is derived with four conflicting objectives, selection of minimum number of sensor nodes, full coverage, connectivity and energy level of the selected sensor nodes. We have introduced a novel mutation operation for better performance and faster convergence of the proposed GA based approaches. We have also formulated the scheduling problem as a Linear Programming. Extensive simulation is performed on various network scenarios by varying number of deployed sensor nodes, target point and network length. We also perform a popular statistical test, analysis of variance followed by post hoc analysis.