Number Of Sensor Nodes Research Articles

A Novel Trust Model Based on Node Recovery Technique for WSN

With the rapid development of sensor technology and wireless network technology, wireless sensor network (WSN) has been widely applied in many resource-constrained environments and application scenarios. As there are a large number of sensor nodes in WSN, node failures are inevitable and have a significant impact on task execution. In this paper, considering the vulnerability, unreliability, and dynamic characteristics of sensor nodes, node failures are classified into two categories including unrecoverable failures and recoverable failures. Then, the traditional description of the interaction results is extended to the trinomial distribution. According to the Bayesian cognitive model, the global trust degree is aggregated by both direct and indirect interaction records, and a novel trust model based on node recovery technique for WSNs is proposed to reduce the probability of failure for task execution. Simulation results show that compared with existing trust models, our proposed TMBNRT (trust model based on node recovery technique) algorithm can effectively meet the security and the reliability requirements of WSN.

A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

The emerging ubiquitous nature of wireless sensor networks has made it suitable and applicable to a diversified number of vital applications that include environment surveillance, health monitoring using implantable sensors, weather forecasting and other plethora of contexts. The critical issues such as computation time, limited memory and energy are more common due to the tiny sized hundred and thousands of sensor nodes existing in the networks. In this context, the network lifetime completely depends on the potential use of available resources. The process of organizing closely located sensor nodes into clusters is convenient for effective management of cluster and improving the lifetime of the complete network. At this juncture, swarm intelligent and evolutionary algorithms the pertains to the problem of NP-complete is determined to achieve a superior optimal solution. In this paper, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed for achieving enhanced network lifetime in sensor networks. In this proposed HABC-BFA-OC technique, the benefits of Bacterial Foraging Optimization is included for improving the local search potential of ABC algorithm in order to attain maximum exploitation and exploration over the parameters considered for cluster head selection. The simulation experiments of the proposed HABC-BFA-OC technique confirmed an enhanced network lifetime with minimized energy consumptions during its investigation with a different number of sensor nodes.

Solving Isolated Nodes Problem in ZigBee Pro for Wireless Sensor Networks

Wireless sensor network based on the ZigBee protocol consists of many sensor devices. In some cases, the sensor nodes may turn to isolated node because random distribution, particularly when creating the network. In this research was suggested two cases to overcome on the isolated node problem, the first case had able to overcome this problem by distributing the isolated nodes on the router nodes that carry the least number of sensor nodes, it helps to minimize the computational overhead on router nodes too, while the second one is able to overcome this problem by calculating the distance between the isolated nodes and the routers and then adds these nodes to the nearest routers. Subsequently, this method helps to minimize the energy consumption. The results show our approach able to solve the problem of isolated nodes using these two methods and when compared between them turns out the second method is better In terms of energy consumption. In addition, we are able to make the network larger scale.

Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks Under Fairness Constraints

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max–min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

RETRACTED ARTICLE: Mobile cluster head selection using soft computing technique in wireless sensor network

Wireless sensor networks generally consist of static sensor node, which can be deployed to monitor the environment. The network is built by the sensor nodes, and the data from the source reach base station by passing through a number of sensor nodes. This causes loss of energy at the sensor nodes. To reduce energy loss of the sensor nodes in WSN, cluster-based topology can be used. Sensor nodes are grouped into clusters. Each cluster contains one cluster head for effective communication. Cluster head collects the data from the sensor nodes and sends to the base station. This causes fast depletion of cluster heads energy. To overcome energy problem, we have proposed a novel mobile data gathering in WSN by soft computing-based CH selection and clustering. It is based on fuzzy inference system. The smart CH selection and vehicular data gathering reduce the time and energy loss due to uploading. Due to reduced energy loss, the lifetime of network is also increased. In this paper, we compared the smart CH selection technique with high-energy CH selection on the quality measures packet loss, collection delay, residual energy, distance travelled and network lifetime. The proposed smart CH selection-based vehicular data gathering is produced better results compared to the other methods.

EELEACH Clustering Approach to Improve Energy Efficiency in WSN

Background: Wireless sensor network is self-organizing which consists of a large number of sensor nodes and one sink node according to recent patents. The most important characteristics of such a network are the restricted resources like battery power, consumption capacity and consumption range. Energy consumption is one of the important issues in the wireless sensor network and the challenge is to prolong the network lifespan. Objective: The objective of the proposed approach is to balance a consumption of energy at member node as well as head node of cluster during the data transmission stage and to improve energy efficiency and lifespan of the network. Methods: The aim of an energy efficient clustering method to deal with the homogenous distribution and deployment of tree structure is performed. The performance of network is enhanced by electing head node with data to the node with greater cluster rate and having lowest distance from sink node. The member node sends their data to the head node which forwards their data to the node with greater weight rate which is sent to the sink node in an energy balancing way. Results: A performance analysis of existing approach as LEACH and proposed approach as EELEACH is undertaken by considering different metrics such as energy consumption successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Conclusion: From result analysis, the proposed system as EELEACH shows successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Hence, the low energy consumption improved lifespan of the network and better data transfer rate.

A New System Model for Sensor Node Validation by Using OPNET

WSN has been massively used in many fields as monitoring devices for several applications; these sensor-nodes can be expected to work hardly for few years without present any technical issues. The validation of sensor nodes is challenged task owning the natural of the environment, the health of this device, replacing or recharging the battery of this tiny device inaccessible place, and the distributions of a huge number of sensor-nodes. However, no validation study has been presented for sensor nodes by using OPNET simulation with ZigBee, with several sensor sets. In this paper, we are designing and implementing a validation model for WSNs to discover bad nodes in any distribution WSN. The result obtained from simulation show that while increasing the number of nodes, number of bad nodes will be increased on one target. However, increase number of targets decrease the number of bad nodes.

On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks.

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

Energy Optimization With Higher Information Quality for SHM Application in Wireless Sensor Networks

Energy is an important resource in wireless sensor nodes which gets depleted soon, especially in harsh environments, where the sensor’s batteries once deployed cannot be reached easily for replacement or recharging activities. Therefore, designing energy-efficient routing protocol for wireless sensor networks is the objective for the applications like structural health monitoring (SHM), military surveillance, and so on. In this paper, energy optimization is performed by the clustering technique. In clustering, the energy harvesting sensor nodes, which are close to the sink and having a higher probability of energy arrival rate, are elected as cluster head nodes. The remaining nonelected node reserves a portion of their harvested energy to be used by them when they are elected as cluster heads. Thus, utilizing this reserved harvested energy, the cluster head nodes can survive longer. Routing the data from cluster head nodes to the sink is done with the shortest path available. Also, sensor node placement optimization is important in SHM applications, as if the desired information quality is achieved with less number of sensor nodes, then it is a cost-effective method. Thus, using the exhaustive search method, the sensor node location optimization is performed in the study presented in this paper.

Experimental Validation of Fuzzy Logic Based Routing Algorithm for Wireless Sensor Network Applied to Indoor Environments

This paper focuses on the experimental validation of the Fuzzy Logic Based Routing Algorithm (FLBRA) proposed for wireless sensor networks which operates in indoor environments in the context of building management systems. The fuzzy logic determines the cost of each link for the routing algorithm and calculates the lower cost path for packet forwarding. The network parameters used to determine the cost of each link through the Fuzzy Logic are the Received Signal Strength Indicator (RSSI), the standard deviation of the RSSI and the packet error rate. The routing algorithm based solely on the RSSI was also tested and compared to the FLBRA one. The proposal performance was evaluated using different scenarios in which different numbers of sensor nodes were considered in the same indoor environment. Both FLBRA and RSSI-based solutions were monitored and analyzed using the same testbed. It can be seen from the results that the FLBRA has a better performance, selecting the best routes with lower cost, in comparison to the solution solely based on the RSSI.

A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks.

Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are arising. Wireless sensor networks (WSNs) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node’s lifetime dictated by the battery’s size. As the form factor of the SN is critical in various use cases, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded and suboptimal decisions about the right hardware components, their configuration, and reliable data communication techniques, such as automatic repeat request (ARQ) and forward error correction (FEC). In this paper, a methodology to design, configure, and deploy a reliable ultra-low power WSNs is proposed. A comprehensive energy model and a realistic path-loss (PL) model of the sensor node are also established. Through estimations and field measurements it is proven that, following the proposed methodology, the designer can thoroughly explore the design space and the make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN.

Minimum Connected Dominating Set Under Routing Cost Constraint in Wireless Sensor Networks With Different Transmission Ranges

Wireless sensor networks (WSNs) are used to cover destination areas for a lot of practical applications. To enhance the performance of the WSN, the virtual backbone based on the connected dominating set is an efficient way with respect to the routing cost between sensors, lifetime of entire network, and so on. In this paper, especially for the WSN with different transmission radii among different sensors, we study the problem of constructing the minimum $\rho $ -range connected dominating set under the constraint $\alpha $ -times of the minimum routing cost ( $\alpha $ MOC- $\rho $ CDS), where $\alpha \ge 5$ and $\rho $ is the ratio of the maximum-to-minimum transmission radius. Our contributions are three folds. First, we propose a polynomial time approximation scheme which generates the $\alpha $ MOC- $\rho $ CDS with the size of at most $(1+\epsilon)$ times of the optimum solution, where $\epsilon $ is the error parameter. Second, we propose a polynomial time algorithm and prove that it has two approximation ratios $(6\rho +1)^{2}(2\rho +1)^{2}$ and $10\lceil ({2\pi }/{\theta })\rceil \lfloor ({\ln 3\rho }/({\ln (1/\cos \theta)})) \rfloor ~\lfloor ({\ln \rho }/({\ln (2\cos (\pi /5))}))\rfloor $ , where $\theta . Finally, we propose the distributed version of the constant approximation ratio algorithm which has both the time complexity and message complexity $O(n^{3})$ , where $n$ is the number of sensor nodes. Besides, the simulation results demonstrate the efficiency of our algorithms.

Localization in three-dimensional wireless sensor networks: a survey

Localization is a process of finding the coordinates of the deployed sensor nodes in the sensing area. Localization in wireless sensor networks (WSNs) is important as it results in location-stamped communication. Applications of WSNs like forest fire detection, nuclear or biological attacks, locating survivors post-disasters, etc. require location-aware information for real time and accurate response. Generally, the sensor nodes are not equipped with global positioning system due to its inefficiency for indoor and underwater regions. Other drawbacks include cost and power consumption. Localization in 3D WSNs is a complex task. The complexity increases due to an additional dimension leading to larger neighboring nodes and increase in coverage area (i.e., area coverage of a sensor node changes from circular to spherical). A lot of approaches have been proposed to localize the sensor nodes in 2D and are now being seen in 3D aspect. Although good efforts have been made for research summarization and guidance in 2D localization, it lacked attention by the researchers in 3D scope. This paper provides a comprehensive survey of the algorithms designed for localizing the sensor nodes in terrestrial and underwater regions. These algorithms have been classified based on the nature of anchor nodes. The localization methods have been categorized as static anchor node-based or mobile anchor node-based and further range-free or range-based depending upon the number of anchor nodes availability and distance estimation technique, respectively. The limitations and challenges of the proposed approaches have also been discussed briefly. The paper also tabulates these methods on the basis of attributes like localization process, complexity, number of sensor nodes used, etc.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks (SMPISCS)

In Wireless Sensor Network (WSN), energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery. This quantified level of co-operation signifies the performance of the network in terms of increased throughput, packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead. The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding. The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate. Semi Markov Process Inspired Selfish aware Co-operative Scheme (SMPISCS) is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network. SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish. Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes, number of sensor nodes and range of detection threshold.

A Review and Analysis of LSSA for Wireless Sensor Networks

Relay node placement algorithm in Wireless Sensor Networks (WSN) for water supply distribution control and monitoring is an important requirement. The researchers have used the connectivity-aware approximation algorithm for relay node placement in WSN. This paper discusses about implementation results of existing Local Search Approximation Algorithm (LSAA). The experimentations have been carried out for varying number of nodes Relay Node Single Cover (RNSC) problem to remove implemented local search approximation algorithm (LSAA). In LSAA algorithm, N number of sensor nodes and M number of relay nodes to allocated for a set cover (SC) for each group. Main contribution of this paper to discuss this work in algorithm form and to make easy and better to related work. To find out the different and approachable result increases the nodes with 100,150,200etc. Mat lab clearly to explain the result in the graphically form.

V-Matrix-Based Scalable Data Aggregation Scheme in WSN

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA) , which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA , as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA , which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.

Modeling and Analyzing Linear Wireless Sensor Networks With Backbone Support

Rapid advancement in micro-electromechanical techniques leads to the wide application of wireless sensor networks (WSNs). In a linear WSN (LWSN), all sensor nodes are arranged in a straight line to monitor health status of some linear infrastructure structure such as bridges, highways, pipelines, etc. To enhance reliability of the infrastructure monitoring services, LWSNs are often designed to incorporate a limited number of backbone nodes for transferring or relaying information, leading to a more complex hybrid structure. In this paper, a multivalued decision diagram (MDD)-based analytical approach is proposed to evaluate performance of an LWSN system with backbone nodes. Particularly, we model and analyze the probability that the hybrid LWSN performs at a particular performance level, which is characterized by the number of sensor nodes being able to reach the base station. A single compact MDD model is constructed by sharing all isomorphic submodel structures involved in different performance levels. The MDD model, once being constructed, can be reused for evaluation using different failure time distributions or mission time. A case study is presented to substantiate the application of the proposed MDD approach for developing the optimal backbone node allocation strategy to guarantee the reliability requirement on the infrastructure monitoring services.

Energy efficient scalability of three level hexagonal heterogeneous broad transmission distance protocol (3L‐HEXA‐HTBTDP) for WSN‐IoT networks

SummaryMultilevel heterogeneity is the present demand of wireless sensor networks as well as internet‐of‐things for balanced energy consumption of the network systems. In this paper, three level hexagonal heterogeneous broad transmission distance protocol (3L‐HEXA‐HTBTDP) is introduced for increasing the energy efficiency of the network. Normal, advanced, and superadvanced sensor nodes in a hexagonal field represent three level heterogeneity. These nodes are elected as cluster heads (CHs) on the basis of new thresholds, thus transferring the collected data to the base station. The protocol is beneficial in cellular communication and web‐of‐things applications to fulfill the high energy requirements of the wireless systems. The scalability is done here for perceiving the differences in the application of proposed algorithm 3L‐HEXA‐HTBTDP on different number of sensor nodes with variable values of initial energies. This is a promising approach to achieve reliable communication, coverage, and connectivity among sensor nodes of the hexagonal field. The scheme is evaluated through exhaustive simulation using Matlab. It is compared with the previous approaches of clustering implemented on the hexagonal field. Different performance parameters like alive node metrics, stability period, network lifetime, residual energy, and variance of energy are used for benchmarking the results of 3L‐HEXA‐HTBTDP with three level hexagonal stable election protocol (3L‐HEXA‐SEP) and three level hexagonal energy efficient heterogeneous clustered scheme (3L‐HEXA‐EEHC). In general, it is observed that the proposed 3L‐HEXA‐HTBTDP outperforms other existing protocols.

Mobile beacon-based adaptive time synchronization for wireless sensor networks

Time synchronization of clocks in the sensor nodes for wireless sensor networks (WSNs) is a fundamental technology for most mission-critical applications. Most of past research in time synchronization for WSNs, however, has only focused on achieving some of the goals at a time, such as accuracy, energy consumption, completion time, etc., making these solutions less capable of adapting to different application requirements. In this paper, we propose a new time synchronization algorithm named MBATS (mobile beacon-based adaptive time synchronization) in which a mobile beacon is employed to move or fly over the sensor deployment area to complete time synchronization. Moreover, MBATS is designed so that the number of sensor nodes that are synchronized by one instance of time synchronization from the mobile beacon could vary dynamically to meet application requirements on accuracy, completion time and energy consumption, making the proposed MBATS algorithm highly adaptable to different application requirements. In addition to showing the advantage of the proposed MBATS algorithm on the adaptability of time synchronization as well as on some of the main metrics of synchronization over comparable schemes for WSNs, we also present the results of our study on comparing the performance of letting the mobile beacon traverse along a designing path versus follow a random path. Such a study is important since it would allow us to learn the performance gains that we can expect to achieve with extra control effort spent on designing the path over the effortless random path strategy. Such study could provide us with some clues on how to choose a suitable time synchronization strategy to better meet application requirements, which may not necessarily be the designed path strategy due to the tradeoff between cost and performance gains.