In this review, we introduce a variety of surface sensitive techniques for the study of organic thin films, and applications to organic devices. These studies include surface plasmon emission light, organic thin film transistors, combination of quartz crystal microbalance and optical waveguide spectroscopy, evaluation of alignment of liquid crystal molecules at surfaces, and biosensor applications.

Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.

Target Q coverage is needed to secure the stability of data collection in WSN. The targets may have different level of importance then the multiple-target coverage scheme must schedule sensors according to each target's weight to increase the network lifetime. The schedule scheme previously proposed for weighted coverage uses an iterative solution to solve the problem but it has long computation time. We propose a heuristic greedy-TQC algorithm to use the residual energy of sensors to generate multiple scheduling cover sets. A simulation shows a dramatic reduction in computation time. The greedy-TQC algorithm is suitable for the frequently topology-changing WSN and for the often changing targets' weights in WSN.

The many-to-one communication nature of wireless sensor networks (WSNs) leads to an unbalanced traffic distribution, and, accordingly, sensor nodes closer to the base station have to transmit more packets than those at the periphery of the network. This problem causes the nodes closer to the base station to deplete their energy prematurely, forming a hole surrounding the base station. This phenomenon is called the energy hole problem, and it severely reduces the network lifetime. In this paper, we present a cooperative power-aware routing algorithm for uniformly deployed WSNs. The proposed algorithm is based on the idea of replacing the constant transmission range of relaying sensor nodes with an adjusted transmission range, in such a way that each individual node consumes its energy smoothly. We formulate the dynamic transmission range adjustment optimization (DTA) problem as a 0-1 Multiple Choice Knapsack Problem (0-1 MCKP) and present a dynamic programming method to solve the optimization problem. Simulations confirm that the proposed method helps to balance the energy consumption of sensor nodes, avoiding the energy hole problem and extending the network lifetime.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.

Concomitantly with the progress of wireless communications, cognitive radio has attracted attention as a solution for depleted frequency bands. Cognitive radio is suitable for wireless sensor networks because it reduces collisions and thereby achieves energy-efficient communication. To make cognitive radio practical, we propose a low-power multi-resolution spectrum sensing (MRSS) architecture that has flexibility in sensing frequency bands. The conventional MRSS scheme consumes much power and can be adapted only slightly to process scaling because it comprises analog circuits. In contrast, the proposed architecture carries out signal processing in a digital domain and can detect occupied frequency bands at multiple resolutions and with low power. Our digital MRSS module can be implemented in 180-nm and 65-nm CMOS processes using Verilog-HDL. We confirmed that the processes respectively dissipate 9.97 mW and 3.45 mW.

We consider how to accurately estimate the position of targets that exist in closed areas such as a room. In the past, arranging the sensors in a straight line would trigger large position estimation errors in the same direction of the straight line. However, this arrangement is useful because of easy setting, wirings, and space limitations. In this paper, we show a novel algorithm which can reduce the error using signals from reflection objects such as walls. The algorithm uses ellipse relations among sensors, targets and reflection points. Using ellipse relations, the algorithm estimates the positions of the reflection points which are assumed to be the locations of the virtual sensors. So in spite of the straight sensor arranging, the sensors are virtually distributed such as surrounding the targets. In this paper, we show the algorithm and error reduction performances as determined from computer simulations.

This letter investigates the cluster lifetime of single-hop wireless sensor networks with cooperative Multi-Input Single-Output (MISO) scheme. The energy consumptions of both intra-cluster and out-cluster communications are considered. Moreover, uniform and linear data aggregations are discussed. It is found the optimal transmission scheme varies with the distance from the cluster to the base station. More interestingly and novelly, the effect of cluster size on the cluster lifetime has been clarified.

For the deployment of sensor networks, the sensor localization, which finds the position of sensor nodes, is very important. Most previous localization schemes generally use the GPS signal for the sensor localization. However, the GPS signal is unavailable when there is an obstacle between the sensor nodes and satellites. Therefore, in this paper, we propose a new localization scheme which does not use the GPS signal. The proposed scheme localizes the sensors by using three mobile anchors. Because the three mobile anchors collaboratively move by themselves, it is self-localizable and can be adopted even when the sensors are randomly and sparsely deployed in the target field.

Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, that is, readings from sensor nodes are aggregated at intermediate nodes to reduce the communication cost. However, the previous optimally secure in-network aggregation protocols against multiple corrupted nodes require two round-trip communications between each node and the base station, including the result-checking phase whose congestion is O(log n) where n is the total number of sensor nodes. In this paper, we propose an efficient and optimally secure sensor network aggregation protocol against multiple corrupted nodes by a random-walk adversary. Our protocol achieves one round-trip communication to satisfy optimal security without the result-checking phase, by conducting aggregation along with the verification, based on the idea of TESLA technique. Furthermore, we show that the congestion complexity, communication complexity and computational cost in our protocol are constant, i.e., O(1).

With Wireless Sensor Networks (WSNs) involving in diverse applications, the realistic analysis of energy consumption of a sensor node in an error-prone network environment is emerging as an elementary research issue. In this paper, we introduce a Distributed Communication Model (DCM) that can accurately determine the energy consumption through data communication from source to destination in error-prone network environments. The energy consumption is affected with the quality of link, which is characterized by symmetry, directivity, instability, and irregularity of the communication range of a sensor node. Due to weak communication links, significant packet loss occurs that affects the overall energy consumption. While other models unable to determine energy consumption due to lossy links in error-prone and unstable network environments, DCM can accurately estimate the energy consumption in such situations. We also perform comprehensive analysis of overheads caused by data propagation through multi-hop distributed networks. We validate DCM through both simulations and experiments using MICAz motes. Similarity of the results from energy consumption analysis with both simulations and experimentations shows that DCM is realistic, compared to other models in terms of accuracy and diversity of the environments.

In the received signal strength-based ranging algorithms, distance is estimated from a path loss model, in which the path loss exponent is considered a key parameter. The conventional RSS-based algorithms generally assume that the path loss exponent is known a priori. However, this assumption is not acceptable in the real world because the channel condition depends on the current wireless environment. In this paper, we propose an accurate estimation method of the path loss exponent that results in minimizing distance estimation errors in varying environments. Each anchor node estimates the path loss exponent for its transmission coverage by the sequential rearrangement of the received signal strengths of all sensor nodes within its coverage. Simulation results show that the proposed method can accurately estimate the actual path loss exponent without any prior knowledge and provides low distance estimation error.

We review recently obtained results for CMOS (Complementary Metal Oxide Semiconductor) imaging devices used in biomedical applications. The topics include dish type image sensors, deep-brain implantation devices for small animals, and retinal prosthesis devices. Fundamental device structures and their characteristics are described, and the results of in vivo experiments are presented.

Temporal and spatial (geographical) fluctuations, which are present in the traffic of wireless sensor networks (WSNs), have a significant affect on the transmission performance and power consumption of WSNs. Several medium access control (MAC) mechanisms have been proposed for IEEE802.15.4 cluster-based WSNs to counter the temporal and spatial traffic fluctuations. However, these mechanisms cannot always achieve simultaneous improvement in both transmission performance and power consumption. In this paper, we propose two enhanced 2-level active period control mechanisms, BI&CAP control and BI&SD&CAP control, to achieve higher system performance than conventional control mechanisms. Various computer simulation results demonstrate the effectiveness of the proposed mechanisms for WSNs with various traffic fluctuations.

This paper presents the design and performance evaluation of a power-controlled topology optimization and channel assignment scheme for Hybrid MAC (abbreviated PTOCA) in wireless sensor networks that require comparatively high data rate communications. In order to maximize the network performance, PTOCA is designed with a cross-layer concept of MAC and network layers, which provides multi-channel TDMA scheduling based on the information of the network topology optimized by transmission power control. The simulation results show that by using the proposed scheme, the network throughput and energy efficiency can be significantly improved. PTOCA is also more effective in improving the network performance when the nodes are uniformly deployed on the sensor field rather than when they are randomly distributed.

This paper proposes ladder-logic programming model for sensor actuator networks. We also demonstrate optimized operations of them with central controller-based device management (CCDM) architecture. A wireless sensor actuator network consists of distributed wireless nodes, and implementing data streams and data processors onto these wireless nodes has been challenging. System programmers have to describe their instructions by a programming language, and data processors must be placed so that it optimizes, for example, total network traffic. The ladder-logic model enables the programming of them, and CCDM makes various types of optimizations feasible, including the optimization of network traffic, delivery latency, load-balancing and fault-tolerance even though these algorithms are not lightweight. In this paper, we focus on traffic reduction case, and propose two moderately complex algorithms. The experiment has shown that CCDM achieves optimizations even with such moderately complex algorithms.

Wireless Sensor Networks (WSNs) are gradually moving toward the adoption of clustered heterogeneous designs, incorporating a mixture of variety kinds of sensor nodes with different radio coverage and battery capacity. Compared with homogeneous networks, heterogeneous networks are able to reduce the initial cost of the network or prolong the network lifetime. The architecture and routing protocol for this type of heterogeneous WSN should be energy aware in order to prolong the lifetime of the network. However, most of the existing clustering methods consider only initial energy of the sensor nodes and ignore the non-uniform energy drainage caused by many-to-one traffic near sink and/or cluster heads in heterogeneous network environment. In this paper, we propose a new clustering method for WSN with heterogeneous node types which selects cluster heads considering not only the transmission power and residual energy of each node but also those of its adjacent nodes. Simulation experiments show that the proposed method increases network lifetime by 80% and 60% more than that of the CC and HEED, respectively.

With exponentially increasing power densities due to technology scaling and ever increasing demand for performance, chip temperature has become an important issue that limits the performance of computer systems. Typically, it is essential to use a set of on-chip thermal sensors to monitor temperatures during the runtime. The runtime thermal measurements are then employed by dynamic thermal management techniques to manage chip performance appropriately. In this paper, we propose an inverse distance weighting method based on a dynamic Voronoi diagram for the reconstruction of full thermal characterization of integrated circuits with non-uniform thermal sensor placements. Firstly we utilize the proposed method to transform the non-uniformly spaced samples to virtual uniformly spaced data. Then we apply three classical interpolation algorithms to reconstruct the full thermal signals in the uniformly spaced samples mode. To evaluate the effectiveness of our method, we develop an experiment for reconstructing full thermal status of a 16-core processor. Experimental results show that the proposed method significantly outperforms spectral analysis techniques, and can obtain full thermal characterization with an average absolute error of 1.72% using 9 thermal sensors per core.

Many methods have been researched to prolong the lifetime of sensor networks that use mobile technologies. In the mobile sink research, there are the track based methods and the anchor points based methods as representative operation methods for mobile sinks. However, most existing methods decrease the Quality of Service (QoS) and lead to routing hotspots in the vicinity of the mobile sinks. The main reason is that they use static mobile sink movement paths that ignore the network environment such as the query position and the data priority. In this paper, we propose a novel mobile sink operation method that solves the problems of the existing methods. In our method, the probe priority of the mobile sink is determined from data priority to increase the QoS. The mobility of sink used to reduce the routing hotspot. Experiments show that the proposed method reduces the query response time and improves the network lifetime much more than the existing methods.

Traffic adaptive 2-level active period control has been proposed to enhance system performance in cluster-based wireless sensor networks (WSNs) employing IEEE 802.15.4 medium access control (MAC) under temporal and spatial (geographical) non-uniform traffic environments. This paper proposes an adaptive method of controlling the backoff window for traffic adaptive 2-level active period control. The proposed method adjusts the size of the backoff window according to the length of the current active period, which is determined by 2-level active period control, and the time position for channel access in the active period. The results evaluated through computer simulations reveal that the proposed method can improve throughput as well as achieve high energy efficiency in cluster-based WSNs with non-uniform traffic distributions.