The booming developments in embedded sensor technique, wireless communication technology, and information processing theory contribute to the emergence of Internet of Things (IoT), which aims at perceiving and connecting the physical world. In recent years, a growing number of Internet-connected sensors have published their real-time state about the real-world objects on the Internet, which makes the content-based sensor search a promising service in the Internet of Things (IoT). However, classical search engines focus on searching for static or slowly varying data, rather than object-attached sensors. Besides, the existing sensor search systems fail to support the search mode based on a given measurement range. Furthermore, accessing all available sensors to find sought targets would result in tremendous communication overhead. Thus an accurate matching estimation mechanism is proposed to support the search mode based on a given search range and improve the efficiency and applicability of existing sensor search systems. A time-dependent periodical prediction method is presented to periodically estimate the sensor output, which combines with the during the period feedback prediction method that can fully exploit the verification information for enhancing the prediction precision of sensor reading to efficiently serve the needs of sensor search service. Simulation results demonstrate that our prediction methods can achieve high accuracy and our matching estimation mechanism can dramatically reduce the communication overhead of sensor search system.

The scan segmentation method is an efficient solution to deal with the test power problem; However, the use of multiple capture cycles may cause capture violations, thereby leading to fault coverage loss. This issue is much more severe in at-speed testing. In this paper, two scan partition schemes based on complex networks clustering ara proposed to minimize the capture violations without increasing test-data volume and extra area overhead. In the partition process, we use a more accurate notion, spoiled nodes, instead of violation edges to analyse the dependency of flip-flops (ffs), and we use the shortest-path betweenness (SPB) method and the Laplacian-based graph partition method to find the best combination of these flip-flops. Beyond that, the proposed methods can use any given power-unaware set of patterns to test circuits, reducing both shift and capture power in at-speed testing. Extensive experiments have been performed on reference circuit ISCAS89 and IWLS2005 to verify the effectiveness of the proposed methods.

Due to the increasing demand for 3D video transmission over wireless networks, managing the quality of experience (QoE) of wireless 3D video clients is becoming increasingly important. However, the variability of compressed 3D video bit streams and the wireless channel condition as well as the complexity of 3D video viewing experience assessment make it difficult to properly allocate wireless transmission resources. In this paper, we discuss the characteristics of H.264 3D videos and QoE assessment of 3D video clients, and further propose a transmission scheme for 3D video transmission over a wireless communication system. The purpose of our scheme is to minimize the average ratio of stalls among all video streaming clients. By taking into account the playout lead and its change, we periodically evaluate the degree of urgency of each client as regards bitstream receipt based on fuzzy logic, and then allocate the transmission resource blocks to clients jointly considering their degrees of urgency and channel conditions. The adaptive modulation and coding scheme (MCS) is applied to ensure a low transmission error rate. Our proposed scheme is suitable for practical implementation since it has low complexity, and can be easily applied in 2D video transmission and in non-OFDM systems. Simulation results, based on three left-and-right-views 3D videos and the Long Term Evolution (LTE) system, demonstrate the validity of our proposed scheme.

Query response times are critical for cluster computing applications in data centers. In this letter, we argue that to optimize the network performance, we should consider the latency of the flows suffered loss, which are called tardy flows. We propose two tardy flow scheduling algorithms and show that our work offers significant performance gains through performance analysis and simulations.

Quadriphase sequences with good correlation properties are required in higher order digital modulation schemes, e.g., for timing measurements, channel estimation or synchronization. In this letter, based on interleaving technique and pairs of mismatched binary sequences with perfect cross-correlation function (PCCF), two new methods for constructing quadriphase sequences with mismatched filtering which exist for even length N ≡ 2(mod4) are presented. The resultant perfect mismatched quadriphase sequences have high energy efficiencies. Compared with the existing methods, the new methods have flexible parameters and can give cyclically distinct perfect mismatched quadriphase sequences.

To help elderly and physically disabled people to become self-reliant in daily life such as at home or a health clinic, we have developed a network-type brain machine interface (BMI) system called “network BMI” to control real-world actuators like wheelchairs based on human intention measured by a portable brain measurement system. In this paper, we introduce the technologies for achieving the network BMI system to support activities of daily living.

In this letter, we present a spectrally efficient multicast method which enables a transmitter to simultaneously transmit multiple multicast streams without any interference among multicast groups. By using unique combiners at receivers with multiple antennas within each multicast group, the proposed method simplifies multiple channels between the transmitter and the receivers to an equivalent channel. In addition, we establish the sufficient condition for the system configuration which should be satisfied for the channel simplification and provide a combiner design technique for the receivers. To remove interference among multicast groups, the precoder for the transmitter is designed by utilizing the equivalent channels. By exploiting time resources efficiently, the channel simplification (CS) based method achieves a higher sum rate than the time division multiplexing (TDM) based method, which the existing multicast techniques fundamentally employ, at high signal-to-noise ratio (SNR) regime. Furthermore, we present a multicast method combining the CS based method with the TDM based method to utilize the benefits of both methods. Simulation results successfully demonstrate that the combined multicast method obtains a better sum rate performance at overall SNR regime.

The unconditionally stable (US) Laguerre-FDTD method has recently attracted significant attention for its high efficiency and accuracy in modeling fine structures. One of the most attractive characteristics of this method is its marching-on-in-order solution scheme. This paper presents Hermite-Rodriguez functions as another type of orthogonal basis to implement a new 2-D US solution scheme.

In this paper, we consider filter-and-forward relay beamforming using orthogonal frequency-division multiplexing (OFDM) in the presence of inter-block interference (IBI). We propose a filter design method based on a constrained max-min problem, which aims to suppress IBI and also avoid deep nulls in the frequency domain. It is shown that IBI can be suppressed completely owing to the employment of beamforming with multiple relays or multiple receive antennas at each relay when perfect channel state information (CSI) is available. In addition, we modify the proposed method to cover the case where only the partial CSI for relay-receiver channels is available. Numerical simulation results show that the proposed method significantly improves the performance as the number of relays and antennas increases due to spatial diversity, and the modified method can make use of the channel correlation to improve the performance.

DDoS remains a major threat to Software Defined Networks. To keep SDN secure, effective detection techniques for DDoS are indispensable. Most of the newly proposed schemes for detecting such attacks on SDN make the SDN controller act as the IDS or the central server of a collaborative IDS. The controller consequently becomes a target of the attacks and a heavy loaded point of collecting traffic. A collaborative intrusion detection system is proposed in this paper without the need for the controller to play a central role. It is deployed as a modified artificial neural network distributed over the entire substrate of SDN. It disperses its computation power over the network that requires every participating switch to perform like a neuron. The system is robust without individual targets and has a global view on a large-scale distributed attack without aggregating traffic over the network. Emulation results demonstrate its effectiveness.

Considering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multi-objective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

It is important to collect and spread accurate information quickly during disasters. Therefore, utilizing Twitter at the time of accidents has been gaining attention in recent year. In this paper, we propose a real-time information sharing system during disaster based on the utilization of Twitter. The proposed system consists of two sub-systems, a disaster information tweeting system that automatically attaches user's current geo-location information (address) and the hashtag of the form “#(municipality name) disaster,” and a disaster information mapping system that displays neighboring disaster-related tweets on a map.

In the European satellite broadcasting specifications, the symbol rate and the carrier frequency are not regulated. Furthermore, the first generation format DVB-S does not have any control signals. In a practical environment, the received signal condition is not stable due to the imperfect reception environment, i.e., unterminated receiver ports, cheap indoor wiring cables etc. These issues prevent correct detection of the satellite signals. For this reason, the conventional signal detection method uses brute force search for detecting the received signal's cyclostationarity, which is an extremely time-consuming approach. A coarse estimation method of the carrier frequency and the bandwidth was proposed by us based on the power spectrum. We extend this method to create a new method for detecting satellite broadcasting signals, which can significantly reduce the search range. In other words, the proposed method can detect the signals in a relatively short time. In this paper, the proposed method is applied to signals received in an actual environment. Our analysis shows that the proposed method can effectively reduce the detection time at almost a same detection performance.

In the current IoT (Internet of Things) environment, more and more Things: devices, objects, sensors, and everyday items not usually considered computers, are connected to the Internet, and these Things affect and change our social life and economic activities. By using IoTs, service providers can collect and store personal information in the real world, and such providers can gain access to detailed behaviors of the user. Although service providers offer users new services and numerous benefits using their detailed information, most users have concerns about the privacy and security of their personal data. Thus, service providers need to take countermeasures to eliminate those concerns. To help eliminate those concerns, first we conduct a survey regarding users' privacy and security concerns about IoT services, and then we analyze data collected from the survey using structural equation modeling (SEM). Analysis of the results provide answers to issues of privacy and security concerns to service providers and their users. And we also analyze the effectiveness and effects of personal information management and protection functions in IoT services.

Recently, multihop wireless sensor networks (WSNs) are widely developed and applied to energy efficient data collections from environments by establishing reliable transmission radio links and employing data aggregation algorithms, which can eliminate redundant transmissions and provide fusion information. In this paper, energy efficiency which consists of not only energy consumptions but also the amount of received data by the base station, as the performance metric to evaluate network utilities is presented for achieving energy efficient data collections. In order to optimize energy efficiency for improvements of network utilization, we firstly establish a graphical game theoretic model for energy efficiency in multihop WSNs, considering message length, practical energy consumptions and packet success probabilities. Afterwards, we propose a graphical protocol for performance optimization from Nash equilibrium of the graphical game theory. The approach also consists of the distributed protocol for generating optimum tree networks in practical WSNs. The experimental results show energy efficient multihop communications can be achieved by optimum tree networks of the approach. The quantitative evaluation and comparisons with related work are presented for the metric with respect to network energy consumptions and the amount of received data by the base station. The performances of our proposal are improved in all experiments. As an example, our proposal can achieve up to about 52% energy efficiency more than collection tree protocol (CTP). The corresponding tree structure is provided for the experiment.

In this paper, we propose an overloaded multiple-input multiple-output (MIMO) signal detection scheme with slab decoding and lattice reduction (LR). The proposed scheme firstly splits the transmitted signal vector into two parts, the post-voting vector composed of the same number of signal elements as that of receive antennas, and the pre-voting vector composed of the remaining elements. Secondly, it reduces the candidates of the pre-voting vector using slab decoding and determines the post-voting vectors for each pre-voting vector candidate by LR-aided minimum mean square error (MMSE)-successive interference cancellation (SIC) detection. From the performance analysis of the proposed scheme, we derive an upper bound of the error probability and show that it can achieve the full diversity order. Simulation results show that the proposed scheme can achieve almost the same performance as the optimal ML detection while reducing the required computational complexity.

This letter presents a simple and explicit formulation of non-unique Wiener filters associated with the linear predictor for processing of sinusoids. It was shown in the literature that, if the input signal consists of only sinusoids and does not include a white noise, the input autocorrelation matrix in the Wiener-Hopf equation becomes rank-deficient and thus the Wiener filter is not uniquely determined. In this letter we deal with this rank-deficient problem and present a mathematical description of non-unique Wiener filters in a simple and explicit form. This description is directly obtained from the tap number, the frequency of sinusoid, and the delay parameter. We derive this result by means of the elementary row operations on the augmented matrix given by the Wiener-Hopf equation. We also show that the conventional Wiener filter for noisy input signal is included as a special case of our description.

Oblivious RAM is a technique for hiding the access patterns between a client and an untrusted server. However, current ORAM algorithms incur large communication or storage overhead. We propose a novel ORAM construction using a matrix logical structure for server storage where a client downloads blocks from each row, choosing the column randomly to hide the access pattern. Both a normal construction and recursive construction, where a position map normally stored on the client is also stored on the server, are presented. We show our matrix ORAM achieves constant bandwidth cost for the normal construction, uses similar storage to the existing Path ORAM, and improves open the bandwidth cost compared to Path ORAM under certain conditions in the recursive construction.

By the deployment of Internet of Things, embedded systems using microcontroller are nowadays under threats through the network and incorporating security measure to the systems is highly required. Unfortunately, microcontrollers are not so powerful enough to execute standard security programs and need light-weight, high-speed and secure cryptographic libraries. In this paper, we port NaCl cryptographic library to ARM Cortex-M0(M0+) Microcontroller, where we put much effort in fast and secure implementation. Through the evaluation we show that the implementation achieves about 3 times faster than AVR NaCl result and reduce half of the code size.

Network Intrusion Detection Systems (NIDS) are deployed to protect computer networks from malicious attacks. Proper evaluation of NIDS requires more scrutiny than the evaluation for general network appliances. This evaluation is commonly performed by sending pre-generated traffic through the NIDS. Unfortunately, this technique is often limited in diversity resulting in evaluations incapable of examining the complex data structures employed by NIDS. More sophisticated methods that generate workload directly from NIDS rules consume excessive resources and are incapable of running in real-time. This work proposes a novel approach to real-time workload generation for NIDS evaluation to improve evaluation diversity while maintaining much higher throughput. This work proposes a generative grammar which represents an optimized version of a context-free grammar derived from the set of strings matching to the given NIDS rule database. The grammar is memory-efficient and computationally light when generating workload. Experiments demonstrate that grammar-generated workloads exert an order of magnitude more effort on the target NIDS. Even better, this improved diversity comes at much smaller cost in memory and speeds four times faster than current approaches.