We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.

It becomes so important to observe a wild life for obtaining not only knowledge of its biological behaviors but also interactions with human beings in terms of geoenvironmental investigation and assessment. A sensor network is considered to be a suitable and powerful tool to monitor and observe a wild life in fields. In order to monitor/observe seabirds, a sensor network is deployed in Awashima island, Japan. A sensor platform is useful for early and quick deployment in fields. Atlas, a server-client type sensor platform, is used with several sensors, i.e., infrared sensors, thermometers within a nest and a sound sensor. The experimental results and the first outcome of observation have been reported. Particularly emphasized is that an infrared sensor has detected a leaving and returning of seabirds, and has identified that a leaving and returning is affected by sunrises and sunsets. An infrared sensed data has also shown a chick's practice before flying to the south. These facts and knowledge have not been clearly obtained by observation of human beings, so have demonstrated the usefulness of sensor networking for ecology observations.

The primary challenges faced by wireless sensor networks are how to construct the shortest spanning tree and how to determine the optimal sink node position in terms of minimizing the data transmission times and their variances for data gathering from all sensor nodes to a sink node. To solve these two problems, we propose a novel algorithm that uses the polygonal affine shortening algorithm with flow aggregation. This algorithm enables a wireless sensor network that has movable sensor nodes and one movable sink node to self-organize the shortest spanning tree and self-determine the optimal sink node position in a fully distributed manner. We also show that our algorithm is faster than the existing shortest path algorithm in terms of computational complexity.

In a wireless sensor network based on the gradient sinking model, unbalanced energy consumption is an inherent problem and can significantly reduce the network lifetime. In this letter, we propose a subcorona-based scheme to analyze the amount of received data and energy consumption of nodes on gradient sinking model. We then design an algorithm to compute the energy consumption of nodes in different subcoronas. Simulation results indicate the correctness of our proposed algorithm.

One of the most challenging issues in wireless sensor networks is extension of the overall network lifetime. Data aggregation is one promising solution because it reduces the amount of network traffic by eliminating redundant data. In order to aggregate data, each sensor node must temporarily store received data, which requires a specific amount of memory. Most sensor nodes use static random access memory (SRAM) or flash memory for storage. SRAM can be implemented in a one-chip sensor node at low cost; however, SRAM requires standby energy, which consumes a lot of power, especially because the sensor node spends most of its time sleeping, i.e. its radio circuits are quiescent. This study proposes two types of divided SRAM: equal-size divided SRAM and equal-ratio divided SRAM. Simulations show that both proposed SRAM types offer reduced power consumption in various situations.

This paper proposes a method for reducing redundant greedy-perimeter transitions in beacon-less geographic routing for wireless sensor networks (WSNs). Our method can be added to existing routing methods. Using a bloom filter, each node can detect a routing loop, and then the node stores the information as “failure history”. In the next forwarding the node can avoid such bad neighbors based on the failure history. Simulation results demonstrate the benefit of our method.

We consider the position estimation system for targets which exist in near wide area. The system has multiple sensors and estimates the position with multiple receivers. In the past, if receivers were arranged on a straight line, the large position error in the same direction of the line is generated. In order to reduce the error, we propose a novel estimation algorithm using transmitter's directivity information. Our system use directional emission made by an array of antennas in a transmitter. In this paper, the error characteristic which should be solved is introduced firstly. After that, our algorithm is presented. Finally the performance of the error reduction is shown by computer simulations. And we also confirm the reduction by experimental trials. The results indicate good reduction of the error.

In Wireless Sensor Networks (WSNs), authentication is a crucial security requirement to avoid attacks against secure communication, and to mitigate against DoS attacks exploiting the limited resources of sensor nodes. Resource constraints of sensor nodes are hurdles in applying strong public key cryptographic based mechanisms in WSNs. To address the problem of authentication in WSNs, we propose an efficient and secure framework for authenticated broadcast/multicast by sensor nodes as well as for outside user authentication, which utilizes identity based cryptography and online/offline signature (OOS) schemes. The primary goals of this framework are to enable all sensor nodes in the network, firstly, to broadcast and/or multicast an authenticated message quickly; secondly, to verify the broadcast/multicast message sender and the message contents; and finally, to verify the legitimacy of an outside user. This paper reports the implementation and experimental evaluation of the previously proposed authenticated broadcast/multicast by sensor nodes scheme using online/offline signature on TinyOS and MICA2 sensor nodes.

Since wireless sensor networks (WSNs) are resources-constrained, it is very essential to gather data efficiently from the WSNs so that their life can be prolonged. Data aggregation can conserve a significant amount of energy by minimizing transmission cost in terms of the number of data packets. Many applications require privacy and integrity protection of the sampled data while they travel from the source sensor nodes to a data collecting device, say a query server. However, the existing schemes suffer from high communication cost, high computation cost and data propagation delay. To resolve the problems, in this paper, we propose a new and efficient integrity protecting sensitive data aggregation scheme for WSNs. Our scheme makes use of the additive property of complex numbers to achieve sensitive data aggregation with protecting data integrity. With simulation results, we show that our scheme is much more efficient in terms of both communication and computation overheads, integrity checking and data propagation delay than the existing schemes for protecting integrity and privacy preserving data aggregation in WSNs.

This paper presents the design and performance evaluation of a delay attack-resilient clock synchronization scheme (abbreviated to DARCS) for wireless sensor networks. In order to provide both synchronization accuracy and robustness, we propose a novel three-way handshake-based protocol, which completely excludes non-deterministic factors such as random backoff durations and unexpected hardware interrupts in a software manner and, in this way, the node can accurately estimate the relative clock offset and the end-to-end delay between a pair of nodes. Consequently, DARCS makes it possible to correct time synchronization errors as well as to detect delay attacks precisely. The simulation results show that DARCS achieves a higher synchronization accuracy and is more resilient to delay attacks than the other popular time synchronization schemes.

Wireless sensor networks are comprised of several sensor nodes that communicate via wireless technology. Locating the sensor nodes is a fundamental problem in developing applications for wireless sensor networks. In this paper, we introduce a distributed localization scheme, called the Rectangle Overlapping Approach (ROA), using a mobile beacon with GPS and a directional antenna. The node locations are computed by performing simple operations that rely on the rotation angle and position of the mobile beacon. Simulation results show that the proposed scheme is very efficient and that the node positions can be determined accurately when the beacon follows a random waypoint movement model.

WSNs (Wireless Sensor Networks) are becoming more widely used in various fields, and localization is a crucial and essential issue for sensor network applications. In this letter, we propose a low-complexity localization mechanism for WSNs that operate in 3D (three-dimensional) space. The basic idea is to use aerial vehicles that are deliberately equipped with anchor nodes. These anchors periodically broadcast beacon signals containing their current locations, and unknown nodes receive these signals as soon as the anchors enter their communication range. We estimate the locations of the unknown nodes based on the proposed scheme that transforms the 3D problem into 2D computations to reduce the complexity of 3D localization. Simulated results show that our approach is an effective scheme for 3D self-positioning in WSNs.

In wireless sensor networks constructed from battery driven nodes, it is difficult to supply electric power to the nodes. Because of this, the power consumption must be reduced. To cope with this problem, clustering techniques have been proposed. EACLE is a method that uses a clustering technique. In EACLE, route selection is executed independently after the CH (Cluster Head) selection. This two-phase control approach increases overheads and reduces the battery power, which shortens the lifetime of wireless sensor networks. To cope with this problem, we have proposed a novel routing and clustering method called PARC for wireless sensor networks that reduces these overheads by integrating the cluster selection phase and the route construction phase into a single phase. However, PARC has a weak point in that the batteries of CHs around the sink node are depleted earlier than the other nodes and the sink node cannot collect sensing data. This phenomenon is called the hot spot problem. In order to cope with this problem of PARC, we propose PARC+, which extends the CH selection method of PARC such as more nodes around the sink can be selected as a CH node. We evaluate our proposed methods by simulation experiments and show its effectiveness.

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.

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.

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.

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.

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.

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.

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).