Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

In indoor localization using sensor networks, performance improvements are required for non-line-of-sight (NLOS) environments in which the estimation error is high. NLOS mitigation schemes involve the detection and elimination of the NLOS measurements. The iterative minimum residual (IMR) scheme, which is often applied to the localization scheme using the time of arrival (TOA), is commonly employed for this purpose. The IMR scheme is a low-complexity scheme and its NLOS detection performance is relatively high. However, when there are many NLOS nodes in a sensor field, the NLOS detection error of the IMR scheme increases and the estimation accuracy deteriorates. Therefore, we propose a new scheme that exploits coarse NLOS detection based on stochastic characteristics prior to the application of the IMR scheme to improve the localization accuracy. Improved performances were confirmed in two NLOS channel models by performing numerical simulations.

This paper discusses the core ambient sensor network (ASN) technologies in view of their support for global connectivity. First, we enumerate ASN services and use cases and then discuss the underlying core technologies, in particular, the importance of the RESTful approach for ensuring global accessibility to sensors and actuators. We also discuss several profile-handling technologies for context-aware services. Finally, we envisage the ASN trends, including our current work for cognitive behavior therapy (CBT) in mental healthcare. We strongly believe that ASN services will become widely available in the real world and an integral part of daily life and society in the near future.

Wireless sensor networks play an important role in several industries. Ad-hoc networks with multi-hop transmissions are considered suitable for wireless sensor networks because of their high scalability and low construction cost. However, a lack of centralized control makes it difficult to respond to congestion when system capacity is exceeded. Therefore, the analysis of system capacity is a critical issue for system design. In this paper, we propose a novel zone division model to analyze the capacity of multi-hop wireless sensor networks using carrier sense multiple access with collision avoidance protocols. We divide the one-hop area to a gateway (GW) into two zones: an outer zone, where access nodes (ANs) can relay packets from multi-hop ANs, and an inner zone where ANs cannot relay packets. Using this approach, we calculate the packet loss for each zone to estimate the capacity, considering the difference in the communication range of the GW and ANs, as well as the collision with hidden nodes. Comparisons with simulation results and the conventional method show that our model achieves higher estimation accuracy.

This paper investigates active period selection for cluster-based WSNs employing traffic adaptive IEEE 802.15.4 beacon enabled medium access control (MAC) under spatial non-uniform traffic and cluster mobility environments, and proposes a simple autonomous distributed superframe duration (SD) selection scheme to enhance network performance under such environments. The proposed scheme autonomously selects an active SD at each cluster head (CH) by using beacon reception power monitoring (conducted in distributed control manner) and also introduces a beacon status notice from sensor nodes (SNs) to their parent CHs in order to prevent unnecessary SD selection at CHs. Moreover, SD reuse mechanism and joint operation with previously proposed distributed backoff mechanism are proposed for the proposed SD selection scheme to further enhance the network performance. The results of computer simulation show that the proposed scheme can improve both the transmission and power efficiency performance of cluster-based WSNs under spatial non-uniform traffic and cluster mobility environments.

In this paper, we propose a modeling approach for wireless sensor networks (WSNs) that is based on non-volatile two-dimensional cellular automata (CA) and analyze the space-time dynamics of a WSN based on the proposed model. We introduce the fourth circuit element with memory function — memristor into the cells of CA to model a non-volatile CA and employ the non-volatile CA in modeling a WSN. A state transition method is designed to implement the synchronous updates of the states between the central sensor nodes and its neighbors which might behave asynchronously in sending messages to the central one. Therefore, the energy consumption in sensor nodes can be reduced by lessening the amount of exchanged information. Simulations demonstrate that the energy consumption of a WSN can be reduced greatly based on the proposed model and the lifetime of the whole network can be increased.

Recently, the growing concepts that information communication technologies apply to social infrastructures have caused deep interests with wireless sensor networks (WSNs). WSNs can be used for various application areas such as home, health, factory and so on. For the different application areas, there are different technical issues (e.g., security, reliability, real time gathering, long life time, scalability). Efficient information gathering can be potentially obtained if we take a suitable information gathering method with considering the requirements of each WSN application. Thus, we have not persisted all information gathering perfectly and have proposed one of simple information gathering methods in response to the requirements of WSN applications in this paper. In the proposed method, the information is converted to physical-layer parameters of wireless communications, such as frequency and time. Also, simulations are performed to validate the effectiveness of the proposed method in real time gathering and estimating with high precision.

In this letter, we propose an energy-efficient in-network processing method for continuous grouped aggregation queries in wireless sensor networks. As in previous work, in our method sensor nodes partially compute aggregates as data flow through them to reduce data transferred. Different from other methods, our method considers group information of partial aggregates when sensor nodes forward them to next-hop nodes in order to maximize data reduction by same-group partial aggregation. Through experimental evaluation, we show that our method outperforms the existing methods in terms of energy efficiency.

This letter presents a method to adaptively counter false data injection attacks (FDIAs) in wireless sensor networks, in which a fuzzy rule-based system detects FDIAs and chooses the most appropriate countermeasures. The method does not require en-route verification processes and manual parameter settings. The effectiveness of the method is shown with simulation results.

Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

In this work, the differential received signal strength based localization problem is addressed. Based on the measurement model, we present the constrained weighted least squares (CWLS) approach, which is difficult to be solved directly due to its nonconvex nature. However, by performing the semidefinite relaxation (SDR) technique, the CWLS problem can be relaxed into a semidefinite programming problem (SDP), which can be efficiently solved using modern convex optimization algorithms. Moreover, the SDR is proved to be tight, and hence ensures the corresponding SDP find the optimal solution of the original CWLS problem. Numerical simulations are included to corroborate the theoretical results and promising performance.

In this letter, we consider the localization problem using received signal strength in wireless sensor networks. Working with a simple non-cooperative scenario in an outdoor localization, we transform the received signal strength measurement model to an alternative optimization problem which is much easier to solve and less complex compared to finding the optimum solutions from the maximum likelihood estimator. Then, we can solve a sequence of nonconvex problems as a range constrainted optimization problem, while the estimated solution also guarantees a monotonic convergence to the original solution. Simulation results confirm the effectiveness of our proposed approach.

In this paper, we propose a novel energy-efficient sensor device management scheme called sensor device personalization (SDP) for the Internet of things (IoT) and wireless sensor networks (WSNs) based on the IEEE 802.15.4 unslotted carrier sense multiple access with collision avoidance (CSMA/CA). In the IoT and WSNs with the star topology, a coordinator device (CD), user devices (UDs), and sensor devices (SDs) compose a network, and the UDs such as smart phones and tablet PCs manage the SDs, which consist of various sensors and communication modules, e.g., smart fridge, robot cleaner, heating and cooling system, and so on, through the CD. Thus, the CD consumes a lot of energy to relay packets between the UDs and the SDs and also has a longer packet transmission delay. Therefore, in order to reduce the energy consumption and packet transmission delay, in the proposed SDP scheme, the UDs obtain a list of SD profiles (including SDs' address information) that the UDs want to manage from the CD, and then the UDs and the SDs directly exchange control messages using the addresses of the SDs. Through analytical models, we show that the proposed SDP scheme outperforms the conventional scheme in terms of normalized throughput, packet transmission delay, packet loss probability, and total energy consumption.

Misbehaving nodes intrinsic to the physical vulnerabilities of ad-hoc sensor networks pose a challenging constraint on the designing of data fusion. To address this issue, a statistics-based reputation method for reliable data fusion is proposed in this study. Different from traditional reputation methods that only compute the general reputation of a node, the proposed method modeled by negative binomial reputation consists of two separated reputation metrics: fusion reputation and sensing reputation. Fusion reputation aims to select data fusion points and sensing reputation is used to weigh the data reported by sensor nodes to the fusion point. So, this method can prevent a compromised node from covering its misbehavior in the process of sensing or fusion by behaving well in the fusion or sensing. To tackle the unexpected facts such as packet loss, a discounting factor is introduced into the proposed method. Additionally, Local Outlier Factor (LOF) based outlier detection is applied to evaluate the behavior result of sensor nodes. Simulations show that the proposed method can enhance the reliability of data fusion and is more accurate than the general reputation method when applied in reputation evaluation.

Wireless sensor networks (WSNs) consist of numerous wireless sensor nodes, each sensor node embedding a tiny communication device enabling the nodes to communicate with each other or the base station. In this paper, we investigate the problem that communication distance must be considered in minimizing the wireless communication energy since the energy consumption is proportional to the 2nd to the 6th power of the distance. Moreover, another problem is that there is a non-uniform energy drain effect in most topologies. Known as the energy hole problem, it can result in premature termination of the entire network. To address these problems, in this paper we first propose a communication routing algorithm that can solve the energy hole problem to the maximum extent possible while minimizing the wireless communication energy by generating an energy efficient spanning tree. This algorithm is beneficial for network lifetimes defined by a high node termination percentage. For the WSNs for which the energy hole problem is critical, we propose two route switching algorithms to solve the energy hole problem; they are beneficial for network lifetimes defined by a low node termination percentage. Simulation results showed that these algorithms avoid the energy hole problem and thereby greatly extend the lifetime of WSNs by more than 3 to 6 times that of ones using direct transmission in a 20-node network and a 50-node network if the lifetime of a WSN is defined by 1% of the number of terminated nodes in the WSN.

As one of the most widely investigated studies in wireless sensor networks (WSNs), multihop networking is increasingly developed and applied for achieving energy efficient communications and enhancing transmission reliability. To accurately and realistically analyze the performance metric (energy efficiency), firstly we provide a measurement of the energy dissipation for each state and establish a practical energy consumption model for a WSN. According to the analytical model of connectivity, Gaussian approximation approaches to experimental connection probability are expressed for optimization problem on energy efficiency. Moreover, for integrating experimental results with theories, we propose the methodology in multihop wireless sensor networks to maximize efficiency by nonlinear programming, considering energy consumptions and the total quantity of sensing data to base station. Furthermore, we present evaluations adapting to various wireless sensor networks quantitatively with respect to energy efficiency and network configuration, in view of connectivity, the length of data, maximum number of hops and total number of nodes. As the consequence, the realistic analysis can be used in practical applications, especially on self-organization sensor networks. The analysis also shows correlations between the efficiency and maximum number of hops, that is the multihop systems with several hops can accommodate enough devices in ordinary applications. In this paper, our contribution distinguished from others is that our model and analysis are extended from experiments. Therefore, the results of analysis and proposal can be conveniently applied to actual networks.

In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.

This paper proposes a reliable data aggregation scheduling that uses caching and re-transmission based on track topology. In the proposed scheme, a node detects packet losses by overhearing messages that includes error indications of the child nodes, from its neighbor nodes. If packet losses are detected, as a backup parent, the node retransmits the lost packet. A retransmission strategy is added into the adaptive timeout scheduling scheme, which adaptively configures both the timeout and the collection period according to the potential level of an event occurrence. The retransmission steps cause an additional delay and power consumption of the sensor nodes, but dramatically increase the data accuracy of the aggregation results. An extensive simulation under various workloads shows that the proposed scheme outperforms other schemes in terms of data accuracy and energy consumption.

More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.

With the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.