Surveillance with multiple cameras and microphones is promising to trace activities of suspicious persons for security purposes. When these sensors are connected to the Internet, they might also jeopardize innocent people's privacy because, as a result of human error, signals from sensors might allow eavesdropping by malicious persons. This paper presents a proposal for exploiting super-resolution to address this problem. Super-resolution is a signal processing technique by which a high-resolution version of a signal can be reproduced from a low-resolution version of the same signal source. Because of this property, an intelligible speech signal is reconstructed from multiple sensor signals, each of which is completely unintelligible because of its sufficiently low sampling rate. A method based on Bayesian linear regression is proposed in comparison with one based on maximum likelihood. Computer simulations using a simple sinusoidal input demonstrate that the methods restore the original signal from those which are actually measured. Moreover, results show that the method based on Bayesian linear regression is more robust than maximum likelihood under various microphone configurations in noisy environments and that this advantage is remarkable when the number of microphones enrolled in the process is as small as the minimum required. Finally, listening tests using speech signals confirmed that mean opinion score (MOS) of the reconstructed signal reach 3, while those of the original signal captured at each single microphone are almost 1.

A number of battery-driven sensor nodes are deployed to operate a wireless sensor network, and many routing protocols have been proposed to reduce energy consumption for data communications in the networks. We have proposed a new routing policy which employs a nearest-neighbor forwarding based on hop progress. Our proposed routing method has a topology parameter named forwarding angle to determine which node to connect with as a next-hop, and is compared with other existing policies to clarify the best topology for energy efficiency. In this paper, we also formulate the energy budget for networks with the routing policy by means of stochastic-geometric analysis on hop-count distributions for random planar networks. The formulation enables us to tell how much energy is required for all nodes in the network to forward sensed data in a pre-deployment phase. Simulation results show that the optimal topology varies according to node density in the network. Direct communication to the sink is superior for a small-sized network, and the multihop routing is more effective as the network becomes sparser. Evaluation results also demonstrate that our energy formulation can well approximate the energy budget, especially for small networks with a small forwarding angle. Discussion on the error with a large forwarding angle is then made with a geographical metric. It is finally clarified that our analytical expressions can obtain the optimal forwarding angle which yields the best energy efficiency for the routing policy when the network is moderately dense.

Wireless sensor networks usually deploy sensor nodes with limited energy resources in unattended environments so that people have difficulty in replacing or recharging the depleted devices. In order to balance the energy dissipation and prolong the network lifetime, this paper proposes a routing spanning tree-based clustering algorithm (RSTCA) which uses routing spanning tree to analyze clustering. In this study, the proposed scheme consists of three phases: setup phase, cluster head (CH) selection phase and steady phase. In the setup phase, several clusters are formed by adopting the K-means algorithm to balance network load on the basis of geographic location, which solves the randomness problem in traditional distributed clustering algorithm. Meanwhile, a conditional inter-cluster data traffic routing strategy is created to simplify the networks into subsystems. For the CH selection phase, a novel CH selection method, where CH is selected by a probability based on the residual energy of each node and its estimated next-time energy consumption as a function of distance, is formulated for optimizing the energy dissipation among the nodes in the same cluster. In the steady phase, an effective modification that counters the boundary node problem by adjusting the data traffic routing is designed. Additionally, by the simulation, the construction procedure of routing spanning tree (RST) and the effect of the three phases are presented. Finally, a comparison is made between the RSTCA and the current distributed clustering protocols such as LEACH and LEACH-DT. The results show that RSTCA outperforms other protocols in terms of network lifetime, energy dissipation and coverage ratio.

We have proposed a fish farm monitoring system for the efficient farming of tuna. In our system, energy efficient and adaptive control of sensor node is highly important. In addition, since a sensor node is attached to the fish, the transmission range of sensor node is not omni-directional. In this paper, we propose a data gathering mechanism for fish farm monitoring by extending a traditional desyncronization mechanism. In our proposed mechanism, by utilizing acknowledgment packets from the sink node, distributed and adaptive timing control of packet transmission is accomplished. In addition, we apply a reassignment mechanism and a sleep mechanism for improving the performance of our proposed mechanism. Through simulation experiments, we show that the performance of our proposed mechanism is higher than that of comparative mechanisms.

Since the sensor nodes are subject to faults due to the highly-constrained resources and hostile deployment environments, fault management in wireless sensor networks (WSNs) is essential to guarantee the proper operation of networks, especially routing. In contrast to existing fault management methods which mainly aim to be tolerant to faults without considering the fault type, we propose a novel efficient fault-aware routing method where faults are classified and dealt with accordingly. More specifically, we first identify each fault and then try to set up the new routing path according to the fault type. Our proposed method can be easily integrated with any kind of existing routing method. We show that our proposed method outperforms AODV, REAR, and GPSR, which are the representative works of single-path routing, multipath routing and location based routing, in terms of energy efficiency and data delivery ratio.

The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

In this paper, a novel fusion center controlled media access control (MAC) protocol for physical wireless parameter conversion sensor networks (PHY-C SN), and a transmission power design for each sensor node are proposed. In PHY-C SN, the sensing information is converted to corresponding subcarrier number of orthogonal frequency division multiplexing (OFDM) signals, and all sensor nodes can send sensing information simultaneously. In most wireless sensor network standards, each sensor node detects the surrounding wireless signal through carrier sense. However, sensor nodes cannot send signals simultaneously if carrier sense is applied in PHY-C SN. Therefore, a protocol for PHY-C SN is devised. In the proposed protocol, the fusion center detects the surrounding wireless environment by carrier sense and requests sensing information transmission toward sensor nodes if no other wireless systems are detected. Once the sensor nodes receive the request signal, they transmit sensing information to the fusion center. Further, to avoid harmful interference with surrounding wireless systems, the transmission power of each sensor is designed to suit the considering communication range and avoid interference toward other wireless systems. The effectiveness of the proposed protocol is evaluated by computer simulation. The parameters for collection like the number of collecting sensor nodes and the radius of the collection area are also examined when determining the transmission power of sensor nodes. Results show that highly efficient information collection with reducing interference both from and towards surrounding wireless systems can be implemented with PHY-C SN.

This paper summarizes Wi-SUN communication systems and their physical (PHY) layer and media access control (MAC) specifications. Firstly, the Wi-SUN communication systems are categorized into three. The key PHY and MAC standards, IEEE 802.15.4g and .4e, that configure the systems are explained, and fundamental transmission performances of the systems in the PHY layer and MAC layer are evaluated by computer simulations. Then, the Wi-SUN alliance and the Wi-SUN profiles that include IEEE 802.15.4g and .4e are explained. Finally, to understand the transmission performance of actual IEEE 802.15.4g Wi-SUN radio devices, PER performances under AWGN and multipath fading environments are measured by using IEEE 802.15.4g compliant and Wi-SUN alliance certified radio modules. This paper is an instruction paper for the beginners of the Wi-SUN based communications systems.

As autonomous underwater vehicles (AUVs) have been widely used to perform cooperative works with sensor nodes for data-gathering, the need for long-range AUVs has further grown to support the long-duration cooperation with sensor nodes. However, as existing data-gathering protocols for the cooperative works have not considered AUVs' energy consumption, AUVs can deplete their energy more quickly before fulfilling their missions. The objective of this work is to develop an AUV based data-gathering protocol that maximizes the duration for the cooperative works. Simulation results show that the proposed protocol outperforms existing protocols with respect to the long-range AUVs.

In this paper, MWAN (Mobile Wireless Ad hoc Networks with internet connection) is considered, which is a solution for many tasks owing to its ease of use, and practicality. Recently, MWAN is required to support large data like multimedia data transfer and it is transferred through several relay nodes. There are 2 problems that cause difficulties for large data transfer through a mobile network. First one is rerouting delay by handoff and second one is network congestion caused by handoff. Also, faulty data transfer caused by handoff delay makes extra load and causes some problems for MWAN. To solve these problems and get network reliability, we propose a new multipath routing scheme that can provide solution for seamless connection while handoff. In the proposed scheme, our MWAN can support multiple paths for data transfer, maintain end-to-end connection while handoff and get new route quickly. The performance of the proposed scheme is evaluated and compared with other multipath routing scheme to show the improvement.

Artificial lighting is responsible for a large portion of total energy consumption and has great potential for energy saving. This paper designs an LED light control algorithm based on users' localization using multiple battery-less binary human detection sensors. The proposed lighting control system focuses on reducing office lighting energy consumption and satisfying users' illumination requirement. Most current lighting control systems use infrared human detection sensors, but the poor detection probability, especially for a static user, makes it difficult to realize comfortable and effective lighting control. To improve the detection probability of each sensor, we proposed to locate sensors as close to each user as possible by using a battery-less wireless sensor network, in which all sensors can be placed freely in the space with high energy stability. We also proposed to use a multi-sensor-based user localization algorithm to capture user's position more accurately and realize fine lighting control which works even with static users. The system is actually implemented in an indoor office environment in a pilot project. A verification experiment is conducted by measuring the practical illumination and power consumption. The performance agrees with design expectations. It shows that the proposed LED lighting control system reduces the energy consumption significantly, 57% compared to the batch control scheme, and satisfies user's illumination requirement with 100% probability.

Wireless sensor networks are being used in many disaster-related applications. Certain types of disasters are studied and modeled with different and dynamic risk estimations in different areas, hence requiring different levels of monitoring. Such nonuniform and dynamic coverage requirements pose a challenge to a sensor coverage problem. This work proposes the Mobile sensor Relocation using Delaunay triangulation And Shifting on Hill climbing (MR-DASH) approach, which calculates an appropriate location for each mobile sensor as an attempt to maximize coverage ratio. Based on a probabilistic sensing model, it constructs a Delaunay triangulation from static sensors' locations and vertices of interesting regions. The resulting triangles are then prioritized based on their sizes and corresponding levels of requirement so that mobile sensors can be relocated accordingly. The proposed method was both compared with an existing previous work and demonstrated with real-world disaster scenarios by simulation. The result showed that MR-DASH gives appropriate target locations that significantly improve the coverage ratio with relatively low total sensors' moving distance, while properly adapting to variations in coverage requirements.

In the evolution of wireless networks such as wireless sensor networks, mobile ad-hoc networks, and delay/disruption tolerant networks, the Store-Carry-Forward (SCF) message relaying paradigm has been commonly featured and studied with much attention. SCF networking is essential for offsetting the deficiencies of intermittent and range limited communication environments because it allows moving wireless communication nodes to act as “mobile relay nodes”. Such relay nodes can store/carry/process messages, wait for a better opportunity for transmission, and finally forward the messages to other nodes. This paper starts with a short overview of SCF routing and then examines two SCF networking scenarios. The first one deals with large content delivery across multiple islands using existing infrastructural transportation networks (e.g., cars and ferries) in which mobility is uncontrollable from an SCF viewpoint. Simulations show how a simple coding technique can improve flooding-based SCF. The other scenario looks at a prototype system of unmanned aerial vehicle (UAV) for high-quality video surveillance from the sky in which mobility is partially controllable from an SCF viewpoint. Three requisite techniques in this scenario are highlighted - fast link setup, millimeter wave communications, and use of multiple links. Through these examples, we discuss the benefits and issues of the practical use of SCF networking-based systems.

This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.

This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.

This paper proposes a Watts and Strogatz-model based routing method for wireless sensor network along with link-exchange operation. The proposed routing achieves low data-collection delay because of hub-node existence. By applying the link exchanges, node with low remaining battery level can escape from a hub node. Therefore, the proposed routing method achieves the fair battery-power consumptions among sensor nodes. It is possible for the proposed method to prolong the network lifetime with keeping the small-world properties. Simulation results show the effectiveness of the proposed method.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

Channel state estimation-based backoff algorithms for channel access are being widely studied to solve wireless channel accessing and sharing problem especially in super dense wireless networks. In such algorithms, the precision of the channel state estimation determines the performance. How to make the estimation accurate in an efficient way to meet the system requirements is essential in designing the new channel access algorithms. In this paper, we first study the distribution and properties of inaccurate estimations using a novel biased estimation analysis model. We then propose an efficient backoff algorithm based on the theory of confidence interval estimation (BA-CIE), in which the minimum sample size is deduced to improve the contention window tuning efficiency, while a fault-tolerance interval structure is applied to reduce the inaccurate estimations so as to improve the contention window tuning accuracy. Our simulation results show that the throughput of our proposed BA-CIE algorithm can achieve 99% the theoretical maximum throughput of IEEE 802.11 networks, thanks to the improved contention window tuning performance.