Randomly distributed wireless sensors used to monitor and detect a moving object were investigated, and performance measures such as the expected time/space detection ratio were theoretically analyzed. In particular, the insensitivities (robustness) of the performance measures to the conditions of the distributed wireless sensors and the target object were analyzed. Robust explicit equations for these performance measures were derived, and these equations can be used to calculate them without knowing the sensing area shape or the target object trajectory. These equations were applied to the following two applications. (1) They were used to estimate the impact of active/sleeping state schedule algorithms of sensors on the expected ratio of the time that the sensors detect the target object during its movement. The results were used to identify the active state schedule that increases the expected time ratio. (2) They were also applied to a sensor density design method that uses a test object. This method can be used to ensure that the expected time ratio that at least one sensor can detect the target satisfies the target value without knowing the sensing area size or the movement of the target object.

CSMA/CA is a well known medium access mechanism extensively used in wireless networks. By detecting the carrier sensing (CS) signal, nodes determine whether the status of the wireless medium is busy or idle. However, recent works have shown that besides detecting the channel status, these signals can be used to derive the transmitted packet size at the nodes in the CS range. In this paper, we present the feasibility of this technique using CC2420 radio. In addition, we show how we can apply larger CS range and packet size detection to solve well-known problems such as reducing latency in the wireless sensor network (WSN). To our knowledge, the proposed solution is the first trial that applies such techniques to design the delay-sensitive scheduling for WSN. Based on our ns-2 simulation, we show that our proposal reduces latency significantly compared to the existing listen/sleep scheduling based protocols.

In wireless sensor networks (WSNs), localization using the received signal strength (RSS) method is famous for easy adaptation and low cost where measuring the distance between sensor nodes. However, in real localization systems, the RSS is strongly affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As a result, the proposed scheme shows that it can provide high precision.

Nucleic acid sensor based on polyaniline has been fabricated by covalently immobilizing double stranded calf thymus (dsCT) DNA onto perchlorate (ClO- 4) doped polyaniline (PANI) film deposited onto indium-tin-oxide (ITO) glass plate using 1-(3-(dimethylamino) propyl)-3-ethylcarbodiimide hydrochloride (EDC)/N-hydroxyl succinimide (NHS) chemistry. These dsCT-DNA-PANI/ITO and PANI/ITO electrodes have been characterized using square wave voltammetry, electrochemical impedance, and Fourier-transform-infra-red (FTIR) measurements. This disposable dsCT-DNA-PANI/ITO bioelectrode is stable for about four months, can be used to detect arsenic trioxide (0.1 ppm) in 30 s.

We have developed an amperometric sensor employing a photoconductive organic thin film that enables the measurement of the two-dimensional distribution of redox current on a sensor surface. The sensor simply consists of photoconductive film and transparent electrode. A focused light beam through the transparent electrode excites the photoconductive film that leads to detect local redox current at the beam position. Intensity of the redox current depends on local concentration of redox species of solution on the sensor. We investigated several materials for the photoconductive film and found a suitable structure is Cu-phthalocyanine doped polyvinylcarbazole film/indium tin oxide/glass substrate. Compared with a conventional two-dimensional chemical sensor, our newly developed sensor can be prepared by lower cost fabrication methods without complex semiconductor processes. The sensor showed a good signal dependence on the concentration of K3Fe(CN)6/K4Fe(CN)6 in an aqueous solution at 15.4 nA/dec at a constant bias voltage of 0.8 V. We measured the two-dimensional distribution of ions in an agarose gel of 2 mm thickness. The result showed a photograph of the diffusion process of redox species. We also discuss the discrimination of redox species like voltammetry.

We have developed novel humidity and gas detector system using quartz crystal oscillators (QCO) deposited with cellulose Langmuir-Blodgett (LB) films. We have realized stable humidity detection by the LB-based QCO sensor for extremely high humidity subsequent to the water dipping condition. Also, specific gaseous molecules such as alcohol could have been sensitively detected.

The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

In a sensor network, a certain area or environment are observed by a lot of distributed sensor nodes and a sink collects data observed by sensor nodes. The observed data sequences that sensor nodes generate may have space- and/or time-dependent correlation. This correlation is regarded as redundant information and can be used for channel error correction by joint decoder using correlation. In this paper, we propose the joint decoding scheme using the time-dependent correlation consisting of the consecutive data sequences generated by a sensor node. We also propose a power control scheme using the time-dependent correlation for reduction in energy consumption. We evaluate the packet error rate and the energy consumption ratio, and clarify the effect of our proposed schemes.

The application of Cumulative Decision Feedback (DF) technique for energy/complexity constrained Wireless Sensor Networks (WSN) is considered. Theoretical bit error probability and average rate of a BPSK modulated DF are derived together with PHY-MAC layers' energy efficiency model for DF and Forward Error Correction (FEC) techniques. Moreover, an empirical optimization, which in turn relies upon a low complexity SNR estimation method also derived in this letter, is applied to the DF technique in order to obtain maximum energy efficiency.

Target tracking is one of the key applications of Wireless Sensor Networks (WSNs) that forms basis for numerous other applications. The overall procedures of target tracking involve target detection, localization, and tracking. Because of the WSNs' resource constraints (especially energy), it is highly desired that target tracking should be done by involving as less number of sensor nodes as possible. Due to the uncertain behavior of the target and resulting mobility patterns, this goal becomes harder to achieve without predicting the future locations of the target. The presence of a prediction mechanism may allow the activation of only the relevant sensors along the future course, before actually the target reaches the future location. This prior activation contributes to increasing the overall sensor networks lifetime by letting non-relevant nodes sleep. In this paper, first, we introduce a Yaw rate aware sensor wAkeup Protocol (YAP) for the prediction of future target locations. Second, we present improvements on the YAP design through the incorporation of adaptability. The proposed schemes are distributive in nature, and select relevant sensors to determine the target track. The performance of YAP and A-YAP is also discussed on different mobility patterns, which confirms the efficacy of the algorithm.

In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information, and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources, parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric classifier, the Gaussian mixture model (GMM) algorithm not only shows good performances to classify targets in WSNs, but it also requires very few resources suitable to a sensor node. In addition, our sensor fusion method uses a decision tree, generated by the classification and regression tree (CART) algorithm, to improve the accuracy, so that the algorithm drives a considerable increase of the classification rate using less resources. Experimental results using a real dataset of WSN show that the proposed scheme shows a 94.10% classification rate and outperforms the k-nearest neighbors and the support vector machine.

Delay profile of ultra-wideband impulse-radio (UWB-IR) indoor channel fluctuates for a physical change such as intruder. This paper investigates a human body detection using the UWB-IR in order to protect a house, not a room, because the radio with high range resolution can penetrate into the inner walls and also the reflected paths from human body are discriminated in time domain. The usefulness is experimentally investigated under a scenario which someone intrudes into a typical house with four rooms and walks around.

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.

Wide area ubiquitous wireless networks, which consist of access points (APs) connected to the fixed network and a great many wireless terminals (WTs), can offer a wide range of applications everywhere. In order to enhance network performance, we need to collect different kinds of data from as many WTs as possible; each AP must be capable of accommodating more than 103 WTs. This requirement can be achieved by employing DSA, a typical centralized media access control scheme, since it has high resource utilization efficiency. In this paper, we propose a novel DSA scheme that employs three new techniques to enhance throughput performance; (1) considering that most terminals tend to send data periodically, it employs both polling-based schemes, i.e. request-polling and data-polling, and a random access scheme. (2) In order to enhance bandwidth utilization effectiveness by polling, the polling timing is decided according to the data generation timing. (3) The AP decides the polled data size according to the latest distribution of data size and polls the WT for the data directly. If the data-polling size can not be determined with confidence, the AP uses request-polling instead of data-polling. Simulations verify that the proposed scheme offers better transmission performance than the existing schemes.

Many applications in wireless sensor networks (WSNs) require the energy efficiency and scalability. Although IEEE 802.15.4/Zigbee which is being considered as general technology for WSNs enables the low duty-cycling with time synchronization of all the nodes in network, it still suffer from its low scalability due to the beacon frame collision. Recently, various algorithms to resolve this problem are proposed. However, their manners to implement are somewhat ambiguous and the degradation of energy/communication efficiency is serious by the additional overhead. This paper describes an Energy-efficient BOP-based Beacon transmission Scheduling (EBBS) algorithm. EBBS is the centralized approach, in which a resource-sufficient node called as Topology Management Center (TMC) allocates the time slots to transmit a beacon frame to the nodes and manages the active/sleep schedules of them. We also propose EBBS with Adaptive BOPL (EBBS-AB), to adjust the duration to transmit beacon frames in every beacon interval, adaptively. Simulation results show that by using the proposed algorithm, the energy efficiency and the throughput of whole network can be significantly improved. EBBS-AB is also more effective for the network performance when the nodes are uniformly deployed on the sensor field rather than the case of random topologies.

Broadcasting is an elementary operation in wireless multi-hop networks. Flooding is a simple broadcast protocol but it frequently causes serious redundancy, contention and collisions. Probability based methods are promising because they can reduce broadcast messages without additional hardware and control packets. In this paper, the counter-based scheme which is one of the probability based methods is focused on as a broadcast protocol, and the RAD (Random Assessment Delay) Extension is proposed to improve the original counter-based scheme. The RAD Extension can be implemented without additional hardware, so that the strength of the counter-based scheme can be preserved. In addition, we propose the additional algorithm called Hop Count Aware RAD Extension to establish shorter path from the source node. Simulation results show that both of the RAD Extension and the Hop Count Aware RAD Extension reduce the number of retransmitting nodes by about 10% compared with the original scheme. Furthermore, the Hop Count Aware RAD Extension can establish almost the same path length as the counter-based scheme.

An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.

In wireless sensor networks, the sensor nodes collect data, which are routed to a sink node. Most of the existing proposals address the routing problem to maximize network lifetime in the case of a single sink node. In this paper, we extend this problem into the case of multiple sink nodes. To maximize network lifetime, we consider the two problems: (i) how to position multiple sink nodes in the area, and (ii) how to route traffic flows from sensor nodes to sink nodes. In this paper, the solutions to these problems are formulated into a Mixed Integer Linear Programming (MILP) model. However, it is computationally difficult to solve the MILP formulation as the size of sensor network grows because MILP is NP-hard. Thus, we propose a heuristic algorithm, which produces a solution in polynomial time. From our experiments, we found out that the proposed heuristic algorithm provides a near-optimal solution for maximizing network lifetime in dense sensor networks.

Two types of thin-film phototransistors (TFPTs), p/i/n TFPT and n/i/n TFPT, are characterized from the viewpoint of operation condition and device behavior. It is found that the detected current can be both independent of the applied voltage (Vapply) and linearly dependent on the photo-illuminance in the saturation region of the p/i/n TFPT. This characteristic is because even if Vapply increases, the depletion layer remains in the whole intrinsic region, and the electric field changes only near the p-type/intrinsic interface and intrinsic/n-type interface but remains in the most intrinsic region. This characteristic is preferable for some kinds of photosensor applications. Finally, an application example of the p/i/n TFPT, artificial retina, is introduced.

Wireless sensor networks are expected to become an important social infrastructure which helps our life to be safe, secure, and comfortable. In this paper, we propose design methodology of an architecture for fast and reliable transmission of urgent information in wireless sensor networks. In this methodology, instead of establishing single complicated monolithic mechanism, several simple and fully-distributed control mechanisms which function in different spatial and temporal levels are incorporated on each node. These mechanisms work autonomously and independently responding to the surrounding situation. We also show an example of a network architecture designed following the methodology. We evaluated the performance of the architecture by extensive simulation and practical experiments and our claim was supported by the results of these experiments.