Subset Of Sensor Nodes Research Articles

Greedy selection of sensors with measurements under correlated noise

We address the sensor selection problem where linear measurements under correlated noise are gathered at the selected nodes to estimate the unknown parameter. Since finding the best subset of sensor nodes that minimizes the estimation error requires a prohibitive computational cost especially for a large number of nodes, we propose a greedy selection algorithm that uses the log-determinant of the inverse estimation error covariance matrix as the metric to be maximized. We further manipulate the metric by employing the QR and LU factorizations to derive a simple analytic rule which enables an efficient selection of one node at each iteration in a greedy manner. We also make a complexity analysis of the proposed algorithm and compare with different selection methods, leading to a competitive complexity of the proposed algorithm. For performance evaluation, we conduct numerical experiments using randomly generated measurements under correlated noise and demonstrate that the proposed algorithm achieves a good estimation accuracy with a reasonable selection complexity as compared with the previous novel selection methods.

Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time.

Comprehensive Cost Optimization for Charger Deployment in Multi-hop Wireless Charging

The multi-hop wireless charging technology can largely extend the charging service range of chargers, thus has promising prospect in sustainable energy replenishment for wireless rechargeable sensor network. This paper proposes a new cost criterion, termed comprehensive cost consisting of energy cost and deployment cost, to measure the actual expenditure of wireless charging. We present a multi-hop wireless charging model and formulate the problem of minimizing the comprehensive cost such that the energy demand of all sensor nodes can be fulfilled by the energy capacitated chargers. We propose a (ln n+1)-approximation algorithm for the optimization problem, where n is the number of sensor nodes. Then, we propose a straightforward cost sharing mechanism, which ensures that no subset of sensor nodes can benefit by breaking away from the current charging tree for any fixed charger position, to realize the paid charging service of multi-hop wireless charging. Furthermore, to keep the magnetic fields of transmitters from the interfering, the conflict avoidance schemes are proposed in both central and distributed situations. Finally, we discuss the distributed scheme for minimizing the comprehensive cost without support of central server. Through extensive simulations, we demonstrate the significant superiority of the proposed algorithms in terms of comprehensive cost.

Analysis rendezvous-based techniques relate to power conservation

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

ANALYSIS RENDEZVOUS-BASED TECHNIQUES RELATE TO POWER CONSERVATION

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

Analysis rendezvous-based techniques relate to power conservation

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory.

This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.

SEC 2: A Secure and Energy Efficient Barrier Coverage Scheduling for WSN-Based IoT Applications

Barrier coverage scheduling is an energy conservation scheme in which a subset of sensor nodes with overlapped sensing area (also called barrier) is activated to meet the key Quality of Service (QoS) requirements such as energy-efficiency, coverage, and connectivity. However, sudden and unexpected node failures in a barrier due to security attacks such as Denial of Service (DoS) poses a challenge for maintaining the desired QoS levels. In this paper, we propose a secure barrier coverage scheduling scheme called SEC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , which prevents QoS degradation in the event of security breaches. This scheme uses a fully weighted attributed dynamic graph model in which a novel attribute-based weight balancing greedy strategy is used to construct barriers. A weighted averaging-based K-means Spectral, and Hierarchical (WKSH) cluster ensemble scheme is proposed to secure a barrier from malicious attacks. WKSH is a graph-based anomaly detection scheme based on weighted Euclidean distance computation and weighted average consensus. The experimental result shows that SEC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> guarantees the required QoS at all times. Moreover, the proposed WKSH shows better accuracy in the classification and detection of attacks in the barrier.

An ADMM-ResNet for data recovery in wireless sensor networks with guaranteed convergence

Data collection is a basic application of wireless sensor networks (WSNs). In practice, only a subset of sensor nodes is selected for data sensing and transmission due to the bandwidth constraint of the channel, energy constraint of the nodes, or malfunctions of the nodes. Data recovery from incomplete sensing data is vital to WSNs. Many works perform data recovery by utilizing the low-rank property of the spatio-temporal correlated data. However, these methods usually converge slowly to achieve satisfactory accuracy performance. In this paper, we propose an ADMM-ResNet framework based on residual networks (ResNets) for spatio-temporal correlated data recovery. The formulated optimization problem is solved by the alternating direction method of multipliers (ADMM) algorithm. The updates of auxiliary variable in the ADMM algorithm can be replaced by ResNets, and the ADMM algorithm is unrolled into a fixed-length neural network. The proposed ADMM-ResNet significantly reduces the number of iterations compared with traditional ADMM algorithm. We theoretically prove that the proposed ADMM-ResNet can globally converge to a fixed-point. Experimental results verify the theoretical convergence and demonstrate the effectiveness.

Virtual grid-based rendezvous point and sojourn location selection for energy and delay efficient data acquisition in wireless sensor networks with mobile sink

Rendezvous points (RPs) based data acquisition methods are widely accepted as the solution for data acquisition delay/latency problem. In these methods, RPs are a subset of sensor nodes, that store the data of other sensor nodes and forward them to the mobile sink (MS). The locations where MS must arrive to acquire data from RPs are called as sojourn locations. RPs are prone to exhaust energy due to the additional activity of forwarding the data and create the energy-hole problem. Although re-selection of RPs mitigates the energy-hole problem, however, it increases the control overhead due to topology reconstruction. Among the various type of RPs selection methods, the grid-based RP selection is a straight-forward and one-time topology construction method. In the existing grid-based RP selection, grid cell coordinators serve as RPs, which are also considered as sojourn locations of MS, and it increases data acquisition latency. This paper proposes a new virtual grid-based rendezvous point and sojourn location selection (VGRSS) method for energy and delay efficient data acquisition that exploits the virtual-grid and constructs an energy efficient search region inside each grid cell. Afterward, it adopts fuzzy interference system to select/re-select RPs from this region. Additionally, the distributed re-selection of RPs for each grid cell reduces between 10 and 30.84% reconstruction overhead of the entire topology. Along with this, the selection of intersection points of four grid cells as sojourn locations of MS decreases between 14 and 31.25% the data acquisition latency in VGRSS. Through simulation results, this paper demonstrates that the VGRSS is efficient over state-of-the-art in terms of energy consumption, control overhead, network lifetime and data acquisition latency.

Target coverage heuristic based on learning automata in wireless sensor networks

In wireless sensors networks, the sensor nodes are densely deployed. Owing to this excessive deployment of sensor nodes, each target is covered by multiple sensors at a time. To prolong the network lifetime, the authors can schedule the sensor activity in such a way that only a subset of sensor nodes, called cover set, is sufficient enough to cover all the targets. In this study, they propose an energy-efficient scheduling algorithm based on learning automata for target coverage problem. The learning automata-based technique helps a sensor node to select its appropriate state (either active or sleep ). To prove the effectiveness of their proposed scheduling method, they conduct a detailed set of simulations and compare the performance of their algorithm with the existing algorithms.

Energy Minimization in Multi-Task Software-Defined Sensor Networks

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Maximum lifetime dependable barrier-coverage in wireless sensor networks

In a wireless sensor network, a subset of sensor nodes provides a barrier-coverage over an area of interest if the sensor nodes are dividing the area into two regions such that any object moving from one region to another is guaranteed to be detected by a sensor node. Recently, Kumar et al. introduced scheduling algorithms for the maximum lifetime barrier-coverage problem. The algorithms achieved the optimal lifetime by identifying a collection of disjoint subsets of nodes such that each subset in the collection can provide barrier-coverage over the area, and by activating each subset in turn. This introduces a new security problem of these scheduling algorithms called barrier-breach. We show there could be a way to penetrate the area protected by barrier-covers when one barrier-cover is replaced by another. To deal with this issue, we propose three different remedies for the algorithms. In addition, we compare the performance of the three approaches against an upper bound via extensive simulation and make a discussion on the results.

Collecting all data continuously in wireless sensor networks with a mobile base station

In this paper, we present a novel coding scheme called separate coding SC for collecting all data continuously in wireless sensor networks with a mobile base station mBS. By separately encoding a certain number of data segments in a combined segment, and storing the combined segments in the corresponding buffers of each sensor node, the SC scheme provides an efficient storage method to collect all data segments. By randomly querying a small subset of sensor nodes, the mBS can reconstruct all the original data segments with high probability. We prove that the success ratio of SC-based data collection is very close to 100%. The number of sensor nodes that should be queried by the mBS can be reduced with a few additional storage space in each sensor node. The performance evaluation has been conducted through computer simulations. It further demonstrates the feasibility and superiority of the proposed SC scheme.

Simulation of Topology Control Algorithms in Wireless Sensor Networks Using Cellular Automata

We use cellular automata for simulating a series of topology control algorithms in Wireless Sensor Networks (WSNs) using various programming environments. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computations using only local information. WSNs are composed of a large number of distributed wireless sensor nodes operating on batteries. The objective of the topology control problem in WSNs is to select an appropriate subset of nodes able to monitor a region at a minimum energy consumption cost and, therefore, extend network lifetime. Herein, we present topology control algorithms based on the selection—in a deterministic or randomized way—of an appropriate subset of sensor nodes that must remain active. We use cellular automata for conducting simulations in order to evaluate the performance of these algorithms and investigate the effect/role of the neighbourhood selection in the efficient application of our algorithms. Furthermore, we implement our simulations in Matlab, Java and Python in order to investigate in which ways the selection of an appropriate programming environment can facilitate experimentation and can result in more efficient application of our algorithms.

Acoustic localization of antbirds in a Mexican rainforest using a wireless sensor network

Acoustic localization is a promising method to passively observe vocal animal species, but remains difficult and time consuming to employ. To reduce the labor intensity and impact of deployment, an acoustic localization system has been developed consisting of battery powered wireless sensor nodes. The system also has the ability to perform an acoustic self-survey, which compares favorably in accuracy to global positioning system survey methods, especially in environments such as forest. The self-survey and localization accuracy of the system was tested in the neotropical rainforest of Chiapas, Mexico. A straight-forward and robust correlation sum localization computation method was utilized and is described in detail. Both free-ranging wild antbird songs and songs played from a speaker were localized with mean errors of 0.199 m and 0.445 m, respectively. Finally, additional tests utilizing only a short segment of each song or a subset of sensor nodes were performed and found to minimally affect localization accuracy. The use of a wireless sensor network for acoustic localization of animal vocalizations offers greater ease and flexibility of deployment than wired microphone arrays without sacrificing accuracy.

A New Approach for Time Synchronization in Wireless Sensor Networks: Pairwise Broadcast Synchronization

This letter proposes an energy-efficient clock synchronization scheme for Wireless Sensor Networks (WSNs) based on a novel time synchronization approach. Within the proposed synchronization approach, a subset of sensor nodes are synchronized by overhearing the timing message exchanges of a pair of sensor nodes. Therefore, a group of sensor nodes can be synchronized without sending any extra messages. This paper brings two main contributions: 1. Development of a novel synchronization approach which can be partially or fully applied for implementation of new synchronization protocols and for improving the performance of existing time synchronization protocols. 2. Design of a time synchronization scheme which significantly reduces the overall network-wide energy consumption without incurring any loss of synchronization accuracy compared to other well-known schemes.

A new method for distributing power usage across a sensor network

We present a method for more uniformly distributing the energy burden across a wireless ground-based sensor network communicating with an overhead unmanned aerial vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, receives and aggregates data gathered by the entire network, and forms a distributed antenna array, concentrating the radiated transmission into a narrow beam aimed towards the UAV. Because these duties are power-intensive, the role of transmit cluster must be shifted to different nodes as time progresses. We present an algorithm to reassign the transmit cluster, specifying the time that should elapse between reassignments and the number of hops that should be placed between successive transmit clusters in order to achieve three competing goals: first, we wish to better and more broadly spread the energy load across the sensor network while, second, minimizing the energy expended in moving the transmit cluster, all the while, third, reducing to the extent practicable the time to bring the UAV and the sensor network’s beam into alignment. Additionally, we present a method for reconfiguring the communication burden between the ground-based sensor network and the UAV. We describe and analyze two alternative strategies to bring the UAV and the sensor network’s beam into alignment, while minimizing the energy expended by the sensor network. The performance of the two strategies is compared in terms of probability of beam-UAV alignment as a function of time, and the expected time to alignment. We examine the performance tradeoff between the choice of strategy and parameters of the sensor network that affect power conservation.

A Self-Localization Method for Wireless Sensor Networks

We consider the problem of locating and orienting a network of unattended sensor nodes that have been deployed in a scene at unknown locations and orientation angles. This self-calibration problem is solved by placing a number of source signals, also with unknown locations, in the scene. Each source in turn emits a calibration signal, and a subset of sensor nodes in the network measures the time of arrival and direction of arrival (with respect to the sensor node's local orientation coordinates) of the signal emitted from that source. From these measurements we compute the sensor node locations and orientations, along with any unknown source locations and emission times. We develop necessary conditions for solving the self-calibration problem and provide a maximum likelihood solution and corresponding location error estimate. We also compute the Cramer-Rao bound of the sensor node location and orientation estimates, which provides a lower bound on calibration accuracy. Results using both synthetic data and field measurements are presented.