In this paper, we propose a novel scheme to optimize the allocation of continuous queries in a sensor network with multiple sinks. The existing scheme compares the coverage areas of given queries and estimates the amount of sharing among them. It tries to allocate queries to the optimal sink that maximizes the amount of sharing and reduces the communication costs among sensor nodes and sinks. However, it inefficiently allocates continuous queries. The amount of sharing among continuous queries depends not only on their coverage area but also on their time-parameters like time-duration and time-interval. We define a new cost estimator with time-parameters for continuous queries and optimize their allocation in the sensor network. Simulation results show that our scheme performs the allocation of continuous queries efficiently and reduces the communication cost.

Skyline queries on sensor networks have attracted much attention from the database research community due to their wide applications related to multi-criteria decision making. The existing methods use filters that are based on the data locality of sensor nodes and routing paths. However, they have two serious problems: i) unnecessary data transmission is still to frequent. ii) the processing cost of a continuous skyline query on high-dimensional data is very high. In this paper, we propose a new method that uses competitive mechanisms for processing continuous skyline queries. The proposed method dramatically reduces the data transmissions of sensors and quickly processes a continuous skyline query on high-dimensional data. An extensive performance study verifies the merits of our new method.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.