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Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Blockchain technology has emerged as a promising solution for addressing trust and security concerns in the Internet of Things (IoT), which is a network of interconnected devices. By deploying blockchain technology at the node level of the IoT, a mutually trusted transaction network can be established between devices, promoting a shift from platform-based to device-based information exchange. However, the implementation of blockchain in wireless IoT scenarios can pose many challenges due to its high resource consumption, including computing power, memory space, and energy consumption. To address these issues, we propose a low-cost algorithm that minimizes the number of blockchain nodes required to meet IoT throughput requirements while ensuring the proper functioning of blockchain, such as guaranteeing consensus success rates in the presence of Byzantine nodes. Our numerical simulation results demonstrate the effectiveness of our proposed scheme in reducing operating costs and promoting the deployment of blockchain in the IoT. [ABSTRACT FROM AUTHOR]

For efficient monitoring of marine environments using underwater wireless sensor networks (UWSNs), a fundamental issue is to maximize network coverage as well as connectivity by appropriate deployment of the nodes within a given sensing area. Most of the existing node deployment schemes proposed for UWSNs, assume that sensor nodes are static and their location after deployment are fixed. However, due to ocean current and the contact of the underwater creatures, these deployed static sensor node moves from its original locations to other locations, which disrupts the network connectivity with the base stations as well as most of the targets remain uncovered. To find a solution to this issue, authors propose a coverage and connectivity aware deployment scheme for optimal placement of a set of autonomous underwater vehicles (AUVs) within UWSNs. The proposed scheme uses an improved Non-dominated Sorting Genetic Algorithm-II based metaheuristic technique with a novel fitness function which contains three parameters namely coverage quality, connected cost and network lifetime. Further, proposed scheme applies an effective encoding scheme for the population representation and devises a novel fitness function for upgrading the quantity of the AUVs as well as their location. Performance estimation of the proposed scheme and its comparison with the existing schemes with respect to convergence rate, coverage quality, connected cost, average energy consumption and network lifetime are discussed in detail. The simulation outcome confirm that the proposed approach upgrades the coverage of the network. [ABSTRACT FROM AUTHOR]

The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations. [ABSTRACT FROM AUTHOR]

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimization algorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimization algorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimization algorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimization algorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimization algorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimization algorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimization algorithm, enhanced grey wolf optimization algorithm, whale optimization algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimization algorithm and bat optimization algorithm is efficient than above stated existing optimization algorithm in terms of average coverage rate. The simulation results also demonstrate that proposed algorithm has attained maximum coverage about 99.25% which is higher as compared to existing algorithms. The standard deviation of proposed algorithm is least i.e. 0.0012 which means proposed algorithm is more reliable as compared to existing algorithms for deploying sensor nodes in three dimensional wireless sensor network. [ABSTRACT FROM AUTHOR]

In recent years, WSNs have become one of the fastest emerging networks. It enables a larger variety of applications in the real-time as well as automation industries. WSN applications are made up of a large count of sensor nodes that are distributed as per the application's requirements. Sensor nodes, depending on its manufacturing rationale, monitor, sense, receive, record, and transfer any type of data. Sensors are inexpensive, tiny, and have limited energy efficiency. Inefficient methods of utilizing this scarce battery power results in the death of nodes which consequently affects the lifetime of the entire network. The failure of nodes because of inadequate routing strategies reduces the network's lifespan and overall quality. Numerous previous research methodologies were applied to improve network lifespan and node connection together with communication dependability. Most of the solutions failed to deliver ideal performance in terms of improving overall QoS, which is a collective characteristic. In this research, a novel WBC approach for data gathering, node clustering, and load balancing in WSN is proposed. The functioning of the proposed model relies on the effective assignment of nodes to the communication task based on their weighted function computed based on the performance characteristic. Load balancing as well as data aggregation, are the two attributes effectively considered in this research work. The performance of the suggested WBC is compared to traditional benchmark techniques using the NS2 program. Multiple measures have been calculated and studied, and in every case, the suggested WBC outperforms. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor network (WSN) has been widely used in many fields. Network coverage is the basis of providing perception services and collecting location information and has become one of the hot topics. For node deployment, this paper proposes two algorithms. One is an improved virtual force (VF) algorithm. The virtual forces of nodes include repulsive force between nodes and repulsive force at the boundary. The improved VF algorithm sets the virtual force threshold. The other is the resampling particle swarm optimization algorithm embedded with virtual force (RPSO-DV). The algorithm combines the advantages of three algorithms, including resampling particle swarm optimization (RPSO) algorithm, particle swarm optimization algorithm based on coefficient adjustment (PSO-D) and improved VF algorithm. In this paper, the two proposed algorithms and reference algorithms in the pieces of literature and are simulated and compared. Firstly, this paper compares the impact of different node numbers and deployment modes on coverage performance in the improved VF algorithm. The simulation shows that the improved VF algorithm can make the network reach a stable state quickly and achieve a high coverage rate. Secondly, this paper lists the confidence intervals for the coverage rate of multiple algorithms at the significance level of 0.05. At the same time, we analyze the specific coverage rate curves and deployment diagrams. The simulation results show that our proposed RPSO-DV algorithm improves the diversity of the population and speeds up the convergence speed. Compared with other reference algorithms, the RPSO-DV algorithm has the highest coverage rate. Finally, this paper analyzes the sensitivity of the parameters of the proposed RPSO-DV algorithm. According to the orthogonal experiment design method, we design 64 sets of experiments. The simulation results show that the algorithm has a certain tolerance and robustness to parameter values. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird's nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for prolonging the network lifetime and reducing the energy being consumed within the wireless sensor networks (WSN). The sensor nodes are distributed in the geographical area in a heterogeneous manner and spread in an advanced and normal mode with high and low energy, respectively. All sensor nodes are organized in groups or clusters. Every cluster has a cluster head (CH) node, and every CH gathers the information from all the sensor nodes, which will be communicated to the base station (BS). Sensor nodes are positioned with initial energy. The network's lifetime and consumed energy are both essential parameters in contrast with the existing routing protocols. It has been accomplished by choosing the proper cluster heads. The mathematical model and simulation results obtained on MATLAB 2015b show a significant fall of the total energy being consumed, now standing at 35%, with the lifetime of the network having been enhanced by 80% as compared to LEACH, Modified-LEACH, and SEP protocol. [ABSTRACT FROM AUTHOR]

For the optimal performance of wireless sensor networks in different areas of applications needs to maximize the coverage area of sensor nodes. The coverage of sensor nodes in monitoring region can be improved by using efficient node deployment algorithms. In this paper node deployment based on bat algorithm (BA) is proposed to enhance the coverage rate of nodes. Each bat describes solution for deployment of sensor nodes individually. In bat algorithm based node deployment grid points covered by one sensor node are excluded for remaining sensor nodes. The benefit of eliminating the grid points is that the load on remaining nodes is decreased and there is no chance of overlapping i.e. grid point is covered by only one sensor node. The simulations of node deployment based on BA and fruit fly optimization algorithm (FOA) are also demonstrated. In this paper to further increase the coverage rate of sensor nodes the performance of various parameters of bat algorithm such as loudness, pulse emission rate, maximum frequency, grid points and sensing radius has been optimized. The simulation results of node deployment based on optimized bat algorithm are also compared with BA and FOA based node deployment in terms of mean coverage rate, computation time and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for optimized bat algorithm, BA and FOA. The results demonstrate the effectiveness of optimized bat algorithm as it achieved more coverage rate than BA and FOA. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) based communication plays a major part in improving the 5G network's system capacity. In pursuance of accomplishing this, effective resource and power allocation algorithms have to be designed for D2D users. As D2D users are considered as secondary users, their interference with cellular users (CUs) should not restrict the communication which is performed based on CU. Most of the existing methodologies performed on bandwidth allocation assume full Channel State Information (CSI) at the base station (BS), which resulted in uncertainty resource allocation. Hence to resolve this, the proposed scheme is developed with an efficient handoff strategy based on bandwidth allocation using Thompson Sampling Bandwidth (TSB) and Magnetic Force Optimization (MFO) for D2D and CU communications along with guaranteed QoS constraint. Here, the available free bandwidth is selected using the TSB technique which in turn is based on overall data rate, level of interference and number of CU users. In this, Magnetic Force optimization (MFO) is utilized for base station selection. This is done based on signal strength variation, coverage area between user and base station, and bandwidth capacity. Thus it results in guaranteed QoS constraint, i.e., parameters like Packet delay; throughput, data rate and reduction in allocated sub-carriers are attained. Finally, the simulation outcomes are performed and the results are estimated in terms of average delay, throughput, node deployment, and data rate. Therefore, the comparative analysis is carried out between the attained outcomes with that of the existing techniques to prove the efficacy of the proposed TSB-MFO technique. [ABSTRACT FROM AUTHOR]

At present the low cost, low power and collaborative feature of Wireless Sensor Network (WSN) is becoming a popular communication technology in smart grid including power generation, transmission and distribution. Among these, the health monitoring of wind power generation system has emerged as one of the many possible applications of WSNs. However the harsh environmental condition of wind farm application brings node deployment as a major design issue in WSN which is well associated with coverage and connectivity issues. Hence the research objective here is twofold. Firstly the sensor nodes are placed optimally in the key components of the wind turbines to improve target coverage. The adjacent turbine span varies with several hundred meters apart which results in independent wireless sensor sub-networks. Connectivity among these sub-networks is a second vital issue, which is guaranteed by joining all the independent sub-networks with the base station by placing minimum number of relay nodes. Hence the connectivity problem is considered as Relay Node Deployment Problem. Connectivity is obtained in this work by bio-inspired Ant Colony Optimization (ACO) algorithm. ACO is further enhanced as ACO-Intelligent Movement, by introducing intelligent movement mechanism. The goal of this approach is to optimize number of relay nodes, decrease deployment cost and to bring up network connectivity. The performance of our novel deployment approach is validated through extensive simulation results. [ABSTRACT FROM AUTHOR]

Node deployment has an influence on energy dissipation of cooperative transmission scheme (CTS) in wireless sensor networks. In order to reduce the energy dissipation in single-hop wireless sensor networks, the problem of node deployment for cooperative beamforming (CBF) based CTS is studied in this paper. First, the energy dissipation for CTS based on CBF is studied, then the node's position and the modulation size is optimized through two-step optimization to minimize energy dissipation. Numerical results show that the optimized node deployment and CBF scheme can greatly reduce the energy dissipation. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm is proposed to optimize the network connectivity efficiency of a network with nodes of different energy harvesting rates by using the fewest RNs while ensuring a high success rate of data transmission. The algorithm calculates the weight of each node based on the energy harvesting capacity and then uses it to calculate the edge weight. Next, based on the edge weight, the Kruskal algorithm is used to create a minimum spanning tree (MST). Finally, the quantity of non-leaf nodes of the MST is inspected to verify that it meets the transmission requirements for data flow. If not, such nodes will be deemed as nodes with a low energy capacity. The support of RNs is required for these nodes to guarantee network connectivity. As shown by experimental data, the algorithm can be used to maintain network connectivity with the fewest RNs, which reduces the cost and increases the transmission success rate of data packages. [ABSTRACT FROM AUTHOR]

This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme. [ABSTRACT FROM AUTHOR]

This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmanned Aerial Vehicles (UAV). With the help of this algorithm, a multi-rotor UAV can reach anywhere in the specific area by charging at the charging station several times. In this algorithm, a number of equal circles are used to cover the specific area (in a two-dimensional plane), and the center of each circle denotes a charging station. The radius of these circles is equal to the radius of action of the UAV. The number of the circles is set by the users. Under the combined effect of three virtual forces, the centers of the circles, called nodes, keep moving within the specific area, and multiple iterations are performed to adjust the location of each node. Finally, a proper deployment scheme for the charging stations is generated, which can achieve the working area maximization of the UAV by a certain number of charging stations. Simulation experiments were executed, and the results under different conditions show that the proposed algorithm can meet the expected requirements and has an advantage over three other algorithms in terms of coverage ratio. The experiment results also indicate that in the case of dense node density, the proposed algorithm has a better coverage performance than the case of sparse node density. The experimental data are available at https://figshare.com/projects/MFA/24064. The codes will be published later. [ABSTRACT FROM AUTHOR]

In this paper, an adjacent coronas based network architecture in which nodes are deployed in accordance with a probability density function (PDF) is proposed. The intrinsic characteristics of the PDF with optimal placement and number of nodes within a corona are determined. The conditions for an energy balanced network are also derived analytically. To confirm the theoretical findings, simulation was carried out in two phases. In the first phase, nodes are deployed as per proposed algorithm and the performance of proposed PDF is compared with that of the other existing distribution techniques. Results of the first phase confirm a significant improvement of up to 83.16% in the average network lifetime with better coverage and connectivity. In the second phase, initially the clustering protocol LEACH is suitably changed and thereafter, the proposed pdf and other existing deployment techniques are executed with LEACH to examine the effectiveness of the proposed algorithm on clustering. Results of the second phase confirm that the proposed algorithm enhances the time to die of the first node up to 16.54% as compared to other existing techniques. Simulation results also confirm that it is possible to obtain energy efficient node distribution in constant area adjacent coronas. [ABSTRACT FROM AUTHOR]

Ease of use, high flexibility and variety of applications have made wireless sensor networks very popular. Node placement in a sensor network is very critical since it affects important network attributes such as coverage, lifetime, and reliability. Therefore, controlled node placement is necessary for achieving specific network features with minimum number of nodes. Since node placement is an NP-hard problem, many placement algorithms have been proposed based on heuristic and meta-heuristic methods. Most of those algorithms assume a uniform event pattern (UEP) throughout the area under investigation. However, in practice some networks deal with non-uniform event pattern (NEP). Optimization of one attribute of the network usually results in degradation of other attributes. That is why in recent studies multi-objective optimization methods have been used which usually provide a set of best answers while the final answer is selected by a decision maker applying a trade-off between all attributes. In this paper a method for controlled node placement is proposed based on multi-objective optimization (MOO) algorithms considering a non-uniform event pattern for the network (NPNEP). In that sense this method is an extension of the previous methods that use uniform event pattern. Because of the good performance of multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), it is utilized as the optimization tool in node placement. In order to use MOEA/D, a few objective functions are defined which can optimize important attributes like coverage, power consumption, delay, reliability and lifetime. In order to achieve optimal node utilization, load balance, and increased lifetime, a cost function for routing is proposed as well as a data gathering and reporting method which both help increase the lifetime of a network. The proposed method can be used for wireless sensor networks with heterogeneous nodes. This algorithm can be used in initial deployment phase as well as operation phase or when problems like fragmentation or loss of coverage occurs. The result of NPNEP algorithm is the initial position of nodes or position of the new nodes which provide the best answers in MOO algorithm. Performance of the proposed algorithm and the effect of NEP as opposed to UEP have been verified by simulations. [ABSTRACT FROM AUTHOR]

In the process of data collection for wireless sensor networks based on large-area multiple nodes, the utilization rate and energy efficiency of existing methods of nodes deployed in the network are relatively low. Therefore we propose hybrid layer-cluster topology method for large multi-node deployment in wireless sensor network in this paper. Data acquisition for each region is assigned to each cluster which is composed of cluster head node and ordinary nodes divided by the hexagon deployment strategy. Star-structure network is adopted between ordinary nodes and cluster head node, each cluster is connected by mesh-structure network. On this basis, the hybrid hop routing method is used to communicate with base station based on critical distance, which saves the cost and improves the efficiency of the network perception. The experimental results show that the layer cluster hybrid topology model based on hexagon partition can reach the coverage rate of 100 % with the energy utilization rate increasing by 5.4 and 8.6 % on the condition of relatively few deployed sensor nodes, compared to quadrilateral partition and random deployment model. This method overcomes the disproportion of the energy consumption and improves the information redundancy in the process of transmission, and can cover the maximum working area along with the longest survival time of the network by reducing energy consumption. This method is an effective solution to the problem of node deployment of large-scale sensor nodes in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, preserving energy requires utmost attention, as they are highly resource constrained. One fundamental way of conserving energy is judicious deployment of nodes within the network for balancing energy flow throughout the network. Node deployment using Gaussian distribution is a standard practice and is widely acceptable when random deployment is used. Initially, an analysis is done to establish that Gaussian distribution based node deployment is not energy balanced. Standard deviation of Gaussian distribution is identified as the parameter responsible for energy balancing. A deployment strategy is proposed for energy balancing using customized Gaussian distribution by discretizing the standard deviation. Performance of the scheme is evaluated in terms of energy balance and network lifetime. Exhaustive simulation is performed to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing. The simulation results show that our scheme also provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with three competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics. [ABSTRACT FROM AUTHOR]

For wireless sensor networks, monitoring large inaccessible areas where deterministic node deployment is not possible, self-organized techniques are in demand to cover an area using optimal number of nodes. In this paper, given an initial random deployment of mobile sensor nodes, we propose a simple and novel technique for self-organized node movement to satisfy the coverage of the given region of interest using a least number of nodes, such that the maximum node displacement is minimized. We present a simple centralized algorithm and also a distributed version of it for node placement. Moreover, in case of a node failure, a distributed fault recovery algorithm is proposed to replace it locally utilizing the available free nodes. Analysis, simulation, and comparison studies show that the proposed algorithms with less neighborhood information result in significant improvement in terms of average and maximum displacement of a node, rounds of communication, and number of active nodes. [ABSTRACT FROM AUTHOR]

Node deployment is a matter of considerable concern in designing wireless sensor networks (WSNs). This paper studies this issue in the context of rechargeable WSNs (RWSNs). We propose an efficient algorithm, namely node deployment for target coverage in RWSNs (NDTCR), which determines the number and positions of installed sensors in two phases. The first phase applies genetic algorithm to construct a mesh over a subset of positions. The mentioned mesh covers the targets and connects them to the sink. In the second phase of NDTCR, we propose an integer linear programming (ILP) model to install some sensors at each position of the mesh. The advantage of applying the first phase is that it prunes the solution space considerably. Therefore, the proposed ILP model can be solved in a reasonable time. The experimental results demonstrate that NDTCR requires 29% fewer sensors on average in comparison with the previous approaches. [ABSTRACT FROM AUTHOR]