Research on Sensor Network Coverage Enhancement Based on Non-Cooperative Games

Coverage is an important issue for resources rational allocation, cognitive tasks completion in sensor networks. The mobility, communicability and learning ability of smart sensors have received much attention in the past decade. Based on the deep study of game theory, a mobile sensor non-cooperative game model is established for the sensor network deployment and a local information-based topology control (LITC) algorithm for coverage enhancement is proposed. We both consider revenue of the monitoring events and neighboring sensors to avoid nodes aggregation when formulating the utility function. We then prove that the non-cooperative game is an exact potential game in which Nash Equilibrium exists. The proposed algorithm focuses on the local information of the neighboring sensors and decides sensors’ next action based on the actions of the other sensors, which maximizes its own utility function. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate that the proposed algorithm can enlarge the coverage of the entire monitoring area while achieving effective coverage of the events.     

Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks

In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly distributed node distribution, a cluster with equal nodes is not an obvious possibility to reduce the energy consumption. To resolve this issue, this paper provides a novel, Balanced-Imbalanced Cluster Algorithm (B-IBCA) with a Stabilized Boltzmann Approach (SBA) that attempts to balance the energy dissipation across uneven clusters in WSNs. BIBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’. So as to handle the changing topological characteristics of sensor nodes, this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes. The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency, lifetime, network stability, average residual energy and so on.     

CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks. Since energy is a scarce resource in sensor networks, it becomes the focus of research, and researchers strive to propose new solutions that are energy efficient. The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization. This paper introduces a Cluster-based Node Recovery (CNR) connectivity restoration mechanism based on the concept of clustering. Clustering is a well-known mechanism in sensor networks, and it is known for its energy-efficient operation and scalability. The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes, while Cluster Heads (CHs) play a significant role in the restoration of connectivity. Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques. The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms. The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.     

Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is secure. In the traditional blockchain, the data layer uses a Merkle hash tree to store data; however, the Merkle hash tree cannot provide non-member proof, which makes it unable to resist the attacks of malicious nodes in networks. To solve this problem, this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and nonmember proof. Moreover, the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements. This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model. Finally, this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.     

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.     

Model Discrimination for Nonlinear Regression Models

Wireless sensor networks (WSNs) are becoming important tools in various tasks, including monitoring and tracking of spatially occurring phenomena. These networks offer the capability of densely covering a large area, but at the same time are constrained by the limiting sensing, processing and power capabilities of their sensors. To complete the task at hand, the information collected by the sensor nodes needs to be appropriately fused. In this article we study the problems of estimating the location of a target and estimating its signal intensity. The proposed algorithms are based on the local vote decision fusion (LVDF) mechanism, where sensors first correct their original decisions using decisions of neighboring sensors. These corrected decisions are more accurate and robust and improve detection; however, they are correlated, which makes maximum likelihood estimation intractable. We adopt a pseudolikelihood formulation and examine several variants of localization and signal estimation algorithms based on original and corrected decisions using direct optimization methods, as well as an EM approach. Uncertainty assessments about the parameters of interest are provided using a parametric bootstrap technique. An extensive simulation study of the developed algorithms, along with several benchmarks, establishes the overall superior performance of the LVDF-based algorithms, especially in low signal-to-noise ratio environments. Extensions to tracking moving targets and localizing multiple targets also are considered.     

Voronoi-based relay placement scheme for wireless sensor networks

Energy consumption is a crucially important issue in battery-driven wireless sensor networks (WSNs). In most sensor networks, the sensors near the data collector (i.e. the sink) become drained more quickly than those elsewhere in the network since they are required to relay all of the data collected in the network to the sink. Therefore more balanced data paths to the sink should be established in order to extend the lifetime of the sensor network. Accordingly, a novel relay deployment scheme for WSNs based on the Voronoi diagram is proposed. The proposed scheme is applicable to both two-dimensional and three-dimensional network topologies and establishes effective routing paths that balance the traffic load within the sensor network and alleviate the burden on the sensors around the sink. Simulation results indicate that the number of relays deployed in the proposed scheme is similar to that deployed in the predetermined location scheme and is significantly less than that deployed in the minimum set cover scheme. Furthermore, the lifetime of the sensor network containing relay nodes deployed using the current scheme is longer than that achieved using either the predetermined location scheme or the minimum set cover scheme.     

Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method.     

A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can provide the data collection necessary for rapid structural assessment after an event such as a natural disaster puts the reliability of civil infrastructure in question. Technical challenges affecting deployment of such a network include ensuring power is maintained at the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data provided by a wireless sensor network. In this work, a new "mobile host" WSN paradigm is presented. This architecture utilizes nodes that are deployed without resident power. The associated sensors operate on a mechanical memory principle. A mobile host, such as a robot or unmanned aerial vehicle, is used on an as-needed basis to charge the node by wireless power delivery and subsequently retrieve the data by wireless interrogation. The mobile host may be guided in turn to any deployed node that requires interrogation. The contribution of this work is the first field demonstration of a mobile host wireless sensor network. The sensor node, referred to as THINNER, capable of collecting data wirelessly in the absence of electrical power was developed. A peak displacement sensor capable of interfacing with the THINNER sensor node was also designed and tested. A wireless energy delivery package capable of being carried by an airborne mobile host was developed. Finally, the system engineering required to implement the overall sensor network was carried out. The field demonstration took place on an out-of-service, full-scale bridge near Truth-or-Consequences, NM.     

Distributed K-coverage self-location estimation scheme based on Voronoi diagram

The success of many wireless sensor network (WSN) applications, such as moving target tracking or environment monitoring, is dependent upon achieving 'k-coverage' of the sensed area, that is every point in the surveillance area is monitored by at least k sensors. This study presents a novel distributed self-location estimation scheme based on a Voronoi diagram to achieve k-coverage in a WSN with mobile nodes. The simulation results show that the proposed scheme effectively to perform k-coverage within the sensing field and fast convergent to fulfil more than 88% k-coverage ratio following three movements for the minimal required sensor deployment.     

Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices

The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services related to IoT. In this paper, we propose a novel distributed range-free algorithm for node localization in wireless sensor networks. The proposed three-dimensional hop localization algorithm is based on the distance error correction factor. In this algorithm, the error decreases with the localization process. The distance correction factor is used at various stages of the localization process, which ultimately mitigates the error. We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm. The simulation results are compared with some of the well-known existing algorithms in the literature. The results show that the proposed three-dimensional error-correction-based algorithm performs better than existing algorithms.     

Energy-efficient directional routing between partitioned actors in wireless sensor and actor networks

Actor?actor communication is an important part of the functioning of wireless sensor?actor networks and enables the actor nodes to take coordinated action on a given event. Owing to various reasons such as actor mobility and low actor density, the actor network tends to get partitioned. The authors propose to use the underlying sensor nodes, which are more densely deployed, to heal these partitions. In order to maximise the utilisation of the limited energy available with the sensor nodes, a new routing protocol for actor?actor communication using directional antennas on the actor nodes is proposed. The authors contribution is threefold. First, using simulations they show that the problem of partitioning in the actor networks is significant and propose an architecture with directional antennas on actor nodes and sensor bridges to heal these partitions. Second, they identify the routing problem for this architecture based on a theoretical framework and propose centralised as well as distributed solutions to it. Third, they develop a routing protocol based on the distributed solution and show, using network simulations, that the proposed protocol not only heals the network partitions successfully, but also achieves high throughput and fairness across different flows, in addition to maximising the network lifetime.     

Controlled deployments for wireless sensor networks

In wireless sensor networks (WSNs), the operation of sensor nodes has to rely on a limited supply of energy (such as batteries). To support long lifetime operation of WSNs, an energy-efficient way of sensor deployment and operation of the WSNs is necessary. A new controlled layer deployment (CLD) protocol to guarantee coverage and energy efficiency for a sensor network is proposed. CLD outperforms previous similar protocols in that it can achieve the same performances and guarantee full area coverage and connection using a smaller number of sensors. It can also ameliorate the 'cascading problem' that reduces the whole network lifetime. Finally, analysis and simulation results show that CLD can use fewer sensor nodes for coverage and also increases the lifetime of the sensor network when compared with the probing environment and adapting sleeping (PEAS) protocol.     

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.     

Plug-n-Play Smart Sensor Network With Dynamic Web Service

In this paper, the authors present an implementation of a plug-n-play web sensor based on a web service approach, utilizing the extensible markup language, which is an open-source software, to exchange information and adopting a low-cost hardware. After an introduction to the web service technology, the problem of the dynamic services and one possible solution will be presented. The proposed solution allows us to have, at all times, an overview of the state of connected sensors and of the available services. Thus, when a new sensor is inserted in the network, its services are automatically “published” without reconfiguring the net; this is important with sensor networks, which are frequently upgraded. The sensor is presented in the framework of a hierarchical distributed system for metrological monitoring applications. Particular attention is devoted to synchronization problems among all composing devices of the proposed distributed system, taking into account the characteristics of main synchronization standards. Measurement results supplied by the sensors are made available as web services so that all users can build up their own applications. To show some preliminary results of the proposed distributed monitoring network, the monitoring of rms values in three-phase power networks was analyzed. A proper synchronization protocol was adopted in order to synchronize the different hardware sections of a sensor to perform a particular measurement.      

空调系统传感器故障检测的无中心算法

本文针对现有空调系统传感器故障检测方法在实时性、适应性等方面的不足,基于建筑无中心智能控制系统,提出了一种诊断传感器慢漂移故障的无中心算法。依据基本物理原理建立约束方程,检验各个变量测量值的一致性,并通过各相邻节点的相互协作,借鉴Hopfield网络中能量函数等相关分析,实现传感器故障检测。该算法无需构建中心节点,不必增加硬件或时序冗余,使数据在系统底层就能自组织地完成诊断,具有并行性和简单性的特点,方便工程实施。以冷冻水二次泵系统为例,对算法进行了验证,结果表明:该方法能实现单个或多个传感器故障的检测,使空调系统传感器故障检测问题得到更合理的解决。     

Local,hierarchic, and iterative reconstructors for adaptive optics

Adaptive optics systems for future large optical telescopes may require thousands of sensors and actuators. Optimal reconstruction of phase errors using relative measurements requires feedback from every sensor to each actuator, resulting in computational scaling for n actuators of n2. The optimum local reconstructor is investigated, wherein each actuator command depends only on sensor information in a neighboring region. The resulting performance degradation "global" modes is quantified analytically, and two approaches are considered for recovering global performance. Combining local and global estimators in a two-layer hierarchic architecture yields computations scaling with n(4/3); extending this approach to multiple layers yields linear scaling. An alternative approach that maintains a local structure is to allow actuator commands to depend on both local sensors and prior local estimates. This iterative approach is equivalent to a temporal low-pass filter on global information and gives a scaling of n(3/2). The algorithms are simulated by using data from the Palomar Observatory adaptive optics system. The analysis is general enough to also be applicable to active optics or other systems with many sensors and actuators.     

A Bayesian network approach for fixture fault diagnosis in launch of the assembly process

Verification and correction of faults related to tooling design and tooling installation are important in the auto body assembly process launch. This paper introduces a Bayesian network (BN) approach for quick detection and localisation of assembly fixture faults based on the complete measurement data set. Optimal sensor placement for effective diagnosis of multiple faults, structure learning of the Bayesian network and the diagnostic procedure are incorporated in the proposed approach. The effective independence sensor placement method is used to reach the desired number of optimal sensor locations, which provide the concise and effective sensor nodes to build the diagnostic Bayesian network. A new algorithm based on conditional mutual information tests is put forward to learn the Bayesian network structure. The body side assembly case was used to illustrate the suggested method and the simulation analysis was performed to evaluate the effectiveness of the diagnostic network. The work demonstrated that the proposed methodology composes a feasible and powerful tool for fixture fault diagnosis in launch of the assembly process.     

Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Wireless sensor networks are increasingly used in sensitive event monitoring. However, various abnormal data generated by sensors greatly decrease the accuracy of the event detection. Although many methods have been proposed to deal with the abnormal data, they generally detect and/or repair all abnormal data without further differentiate. Actually, besides the abnormal data caused by events, it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources. Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events. In this paper, we propose a data cleaning approach based on Stacked Denoising Autoencoders (SDAE) and multisensor collaborations. We detect all abnormal data by SDAE, then differentiate the abnormal data by multi-sensor collaborations. The abnormal data caused by events are unchanged, while the abnormal data caused by other factors are repaired. Real data based simulations show the efficiency of the proposed approach.     

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance.