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.

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.

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.

An all-digital time-domain ADC, abbreviated as TAD, is presented in this paper. All-digital structure is intrinsically compatible with the scaling of CMOS technology, and can satisfy the great demand of miniaturized and low-voltage sensor interface. The proposed TAD uses an inverter-based Ring-Delay-Line (RDL) to transform the input signal from voltage domain to time domain. The voltage-modulated time information is then digitized by a composite architecture namely “4-Clock-Edge-Shift Construction” (4CKES). TAD features superior voltage sensitivity and 1st-order noise shaping, which can significantly simplify the power-hungry pre-conditioning circuits. Reconfigurable resolution can be easily achieved by applying different sampling rates. A TAD prototype is fabricated in 65nm CMOS, and consumes a small area of 0.016mm2. It achieves a voltage resolution of 82.7µV/LSB at 10MS/s and 1.96µV/LSB at 200kS/s in a narrow input range of 0.1Vpp, merely under 0.6V supply. The highest SNR of TAD prototype is 61.36dB in 20kHz bandwidth at 10MS/s. This paper also analyzes the nonideal effects of TAD and discusses the potential solutions. As the principal drawback, nonlinearity of TAD can be compensated by the differential-setup and digital calibration.

A quartz-crystal-microbalance (QCM) and surface-plasmon-resonance (SPR) hybrid sensor was prepared, and the depositions of polymer electrolytes layer-by-layer (LbL) films were observed in situ. The estimated thicknesses obtained from the QCM method were different from those obtained from the SPR method. This was estimated to be caused by film swelling and water contained in the film.

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.

The spatial relations between sensors placed for target detection can be inferred from the responses of individual sensors to the target objects. Motivated by this fact, this paper proposes a method for estimating the location of sensors by using their responses to target objects. The key idea of the proposal is that when two or more sensors simultaneously detect an object, the distances between these sensors are assumed to be equal to a constant called the basic range. Thus, new pieces of proximity information are obtained whenever an object passes over the area in which the sensors are deployed. This information is then be aggregated and transformed into a two dimensional map of sensors by solving a nonlinear optimization problem, where the value of the basic range is estimated together. Simulation experiments verify that the proposed algorithm yields accurate estimates of the locations of sensors.

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.

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.

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.

A 1-mm-diameter fiber-optic photoreceiver with a side-surface interface is proposed. By controlling the scattering part embedded in the fiber, the receiving sensitivity along the fiber's axis is successfully flattened over a 5-m-length. The simulation results suggest a potential for a large-area photo-detector of $sim$ 3-m-spherical diameter.

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.

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.

We propose a temperature sensor employing a ring oscillator composed of poly-Si thin-film transistors (TFTs). Particularly in this research, we compare temperature sensors using TFTs with lightly-doped drain structure (LDD TFTs) and TFTs with offset drain structure (offset TFTs). First, temperature dependences of transistor characteristics are compared between the LDD and offset TFTs. It is confirmed that the offset TFTs have larger temperature dependence of the on current. Next, temperature dependences of oscillation frequencies are compared between ring oscillators using the LDD and offset TFTs. It is clarified that the ring oscillator using the offset TFTs is suitable to detect the temperature. We think that this kind of temperature sensor is available as a digital device.

We propose a method to extract scene situation by orientation sensors of multiple mobile phones' environment. By using orientations recorded with videos, we analyzed their view concentrations as a remarkable position of the scene for each frame of videos. In an experiment for a soccer scene, the extracted points can be related to a trajectory of a soccer ball.

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.

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.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.