International Journal of Distributed Sensor Networks

Localization is one of the essential issues in wireless sensor networks. Randomly deployed sensor nodes that do not own any positioning device need to determine their locations for further purposes. Range-based schemes typically utilize extra hardware to provide higher accuracy based on either node-to-node distances or angles. On the other hand, range-free mechanisms support coarse localization without the specific equipment. The paper presents a range-free localization scheme with aerial anchor nodes. Each aerial anchor flies across the sensing field and also periodically broadcasts its location information obtained from its GPS receiver. The sensor nodes use the information and geometry principles to calculate their positions. With the mechanism, the sensor nodes do not need distance-probing hardware or additional communication among them. The localization mechanism has been evaluated and the results show that the mechanism had better performance than other range-free mechanisms.

Distributed multi-input multi-output (MIMO) system is a promising architecture to provide reliable communications over spatially separated relaying nodes. In this paper, we will investigate the optimum resource allocation techniques in distributed MIMO systems, employing differential (de)modulation and various relaying protocols. Instead of limiting to energy optimization, we solve this problem via a two-dimensional energy and location optimization. The benefits of our resource optimization approaches are illustrated through extensive analysis and simulations. Both the comparisons between different optimization techniques and systems with different protocols are included.

Based on our previous work, we provide an upper bound of the symbol error rate (SER) for decode-and-forward (DF) systems and an approximated SER for amplify-and-forward (AF) systems. We proved that the system SER is a convex function in both the transmit energy and the relay location, and then carried out the two-dimensional energy and location optimization through numerical search. As we know, system energy is a limited resource, especially for mobile terminals, and the coverage area is a critical factor to judge the system performance. Therefore, we evaluate the benefits of our optimization techniques in terms of the system energy saving and the coverage distance extension.

The simulations reveal several interesting results. For both the DF and the AF protocols, the optimized systems always outperform the unoptimized systems with either less energy consumption or longer transmission range. It is also noticed that the benefits of both energy and location optimizations vary a lot for different protocols, and with different system configurations. Uniform energy allocation and midpoint relay location are normally chosen as an initial system setup. For such a configuration with the DF protocol, location optimization is more critical than energy optimization, and the unoptimized system receives prominent benefits from both optimizations, and tremendous system resources savings. For the AF protocol, however, the location and energy optimizations are equally important for the unoptimized system. It turns out that the uniform energy allocation and the midpoint relay location result in fairly good system performance, since it is reasonably close to the global optimum.

For other initial system setups, the optimization benefits are also distinct in the AF and the DF systems. In DF systems, more optimization benefits can be achieved when the relays are either close to the destination or have more transmit energy allocated to the relay(s). On the contrary, in the AF systems, remarkable optimization benefits will be achieved when the relays are far from the midpoint, or when the relays are only able to transmit at low energy levels.

Largescale scientific applications require using various system resources to execute complex computing pipelines in distributed networks to support collaborative research. System resources are typically shared in the Internet or over dedicated connections based on their location, availability, capability, and capacity. Optimizing the network performance of computing pipelines in such distributed environments is critical to the success of these applications.

We consider two types of largescale distributed applications: (i)

interactive applications where a single dataset is sequentially processed along a pipeline; and

The computing pipelines of these applications consist of a number of modules executed in a linear order in network environments with heterogeneous resources under different constraints. Our goal is to find an efficient mapping scheme that allocates the modules of a pipeline to network nodes for minimum endtoend delay or maximum frame rate.

We formulate the pipeline mappings in distributed environments as optimization problems and categorize them into six classes with different optimization goals and mapping constraints: (i)

Minimum Endtoend Delay with No Node Reuse (MEDNNR),

Here, “contiguous node reuse” means that multiple contiguous modules along the pipeline may run on the same node and “arbitrary node reuse” imposes no restriction on node reuse. Note that in interactive applications, a node can be reused but its resource is not shared.

We prove that MEDANR is polynomially solvable and the rest are NP-complete. MEDANR, where either contiguous or noncontiguous modules in the pipeline can be mapped onto the same node, is essentially the Maximum n-hop Shortest Path problem, and can be solved using a dynamic programming method. In MEDNNR and MFRNNRS, any network node can be used only once, which requires selecting the same number of nodes for onetoone onto mapping. We show its NP-completeness by reducing from the Hamiltonian Path problem. Node reuse is allowed in MEDCNR, MFRCNRS and MFRANRS, which are similar to the Maximum n-hop Shortest Path problem that considers resource sharing. We prove their NP-completeness by reducing from the Disjoint-Connecting-Path Problem and Widest path with the Linear Capacity Constraints problem, respectively.

Sensor networks require efficient, low latency key management techniques that enable strong security and tolerance of node compromise. Conventional interactive approaches using public key certificate-based key management techniques are not communications efficient and are very time-consuming. Protocols that leverage elliptic curve cryptography reduce communications but still require considerable interactive exchange. Noninteractive techniques that leverage identity-based public-key cryptography show considerable promise, but these techniques are relatively immature and require considerable computations. Conversely, random key predistribution techniques reduce computations, but at the expense of many interactions. In this paper, we describe recent work in the cryptographic community that combines the benefits of both identity-based cryptography and random-key predistribution into a framework we call identity-based random-key predistribution (IBRKP). IBRKP establishes pair-wise keys with virtually no extra communications and provides security versus node memory trade-offs for the sensor network designer to engineer.

An increasing number of high performance research network testbeds and production networks have the capability of provisioning dedicated channels for high speed data transfer in support of large scale scientific applications. Each dedicated channel in these networks typically consists of one or more physical links that are shared by multiple applications in both time and bandwidth through in advance reservation. The design of efficient bandwidth scheduling algorithms is critical to maximizing the utilization of dedicated network resources and meeting diverse end-to-end transport performance requirements.

The topology of a dedicated network is represented by a graph, where each link maintains a list of residual bandwidths specified as segmented constant functions of time. Given a graph with an aggregated time-bandwidth table combining the reservation information on all links, designated source, and destination vertices, and a specified data size, we formulate and investigate five bandwidth scheduling problems to minimize the data transfer end time under different path and bandwidth constraints: (i)

variable path with variable bandwidth (VPVB), which computes the widest (highest bandwidth) path in each time slot;

Information fusion problems have a rich history spanning four centuries and several disciplines as diverse as political economy, reliability engineering, target tracking, bioinformatics, forecasting, distributed detection, robotics, cyber security, nuclear engineering, distributed sensor networks, and others. Over the past decade, the area of information fusion has been established as a discipline by itself with several contributions to its foundations as well as applications. In a basic formulation of the information fusion problem, each component is characterized by a probability distribution. The goal is to estimate a fusion rule for combining the outputs of components to achieve a specified objective such as better performance or functionality compared to the components. If the sensor error distributions are known, several fusion rule estimation problems have been formulated and solved using deterministic methods.

In the area of pattern recognition a weighted majority fuser was shown to be optimal in combining outputs from pattern recognizers under statistical independence conditions. A simpler version of this problem corresponds to the Condorcet Jury theorem proposed in 1786. This result was rediscovered since then in other disciplines including by von Neumann in 1959 in building reliable computing devices. The distributed detection problem, studied extensively in the target tracking area, can be viewed as a generalization of the above two problems. In these works, the underlying distributions are assumed to be known, which is quite reasonable in the areas these methods are applied.

In a different formulation, we consider estimating the fuser based on empirical data when no information is available about the underlying distributions of components. Using the empirical estimation methods, this problem is shown to be solvable in principle, and the fuser performance may be sharpened based on the specific formulation. The isolation fusers perform at least as good as best component, and the projective fusers perform as good as best combination of components. In a special case of function estimation, each component could be a potential function estimator, radial basis function, nearest neighbor estimator, regressogram, kernel estimator, regression tree or another estimator.

A projective fuser based on a nearest neighbor concept has been proposed based on Voronoi regions in this case. Under fairly general smoothness and non-smoothness conditions on the individual estimators, the expected fuser error is close to optimal with a specified probability. This result is purely sample-based and distribution-free in that it does not require the knowledge of underlying distributions and can be computed only using measurements.

This paper presents a new approach for distributed target detection in wireless sensor networks (WSNs). Contrary to the conventional practice where every sensor uses an identical threshold for decision-making, an unequal and dynamic local sensor threshold selection scheme is proposed. This threshold selection scheme is based on a recently proposed statistical metric for multiple testing problems called the False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like the system level probability of detection and the probability of false alarm are studied. Simulation results are provided to demonstrate the effectiveness of this approach.

In dense wireless sensor networks, density control is an important technique for prolonging the network's lifetime while providing sufficient sensing coverage. In this paper, we develop three new density control protocols by considering the tradeoff between energy usage and coverage. The first one, Non-Overlapping Density Control, aims at maximizing coverage while avoiding the overlap of sensing areas of active sensors. For the ideal case, a set of optimality conditions are derived to select sensors such that the sensing space is covered systematically to maximize the usage of each sensor and minimize the coverage gap. Based on theoretical optimality conditions, we develop a distributed protocol that can be efficiently implemented in large sensor networks. Next, we present a protocol called Non-Overlapping Density Control Based on Distances that does not require location information of the nodes. This protocol is more flexible and easier to implement than existing location-based methods. Finally, we present a new range-adjustable protocol called Non-Overlapping Density Control for Adjustable Sensing Ranges. It allows heterogenous sensing ranges for different sensors to save energy consumption. Extensive simulation shows promising results of the new protocols.

The high-performance networking requirements for next generation large-scale applications belong to two broad classes: a) high bandwidths, typically multiples of 10Gbps, to support bulk data transfers, and b) stable bandwidths, typically at much lower bandwidths, to support computational steering, remote visualization, and remote control of instrumentation.

Current Internet technologies, however, are severely limited in meeting these demands because such bulk bandwidths are available only in the backbone, and stable control channels are hard to realize over shared connections. The UltraScience Net (USN) facilitates the development of such technologies by providing dynamic, cross-country dedicated 10Gbps channels for large data transfers, and 150 Mbps channels for interactive and control operations. Contributions of the USN project are two-fold:

Infrastructure Technologies for Network Experimental Facility: USN developed and/or demonstrated a number of infrastructure technologies needed for a national-scale network experimental facility. Compared to Internet, USN's data-plane is different in that it can be partitioned into isolated layer-1 or layer-2 connections, and its control-plane is different in the ability of users and applications to setup and tear down channels as needed. Its design required several new components including a Virtual Private Network infrastructure, a bandwidth and channel scheduler, and a dynamic signaling daemon. The control-plane employs a centralized scheduler to compute the channel allocations and a signaling daemon to generate configuration signals to switches. In a nutshell, USN demonstrated the ability to build and operate a stable national-scale switched network.

In today's global economy, shrinking business cycles and global competetition are changing the landscape of software development. Frequently, multiple and geographically dispersed development teams are working together on large complex software systems. The complexity of global software development (GSD) warrants investigation into practices for building highly effective geographically diverse teams. GSD introduces challenges such as physical distance, time-zone differences, and diversity of languages and cultures. Frequently, different parts of an organization might use different terms and naming schemes and may have unique ways of expressing concepts. Knowledge of dependencies between software components and a variety of technical platforms must be recognized and shared across the organization. Such a global environment requires a high degree of collaboration and knowledge sharing. The use of a flexible, user-friendly knowledge management system that allows natural language query processing across knowledge captured throughout the software development lifecycle (SDLC) is required to improve knowledge sharing and reuse.

A variety of knowledge management techniques can be introduced into the SDLC to enhance the understanding and knowledge sharing between dispersed team members. Within the commercial realm, applications to capture and effectively disseminate knowledge are becoming more prevalent; however, many are specific to a business domain rather than focused on facilitating knowledge sharing across the SDLC.

A high level design of a system that provides an integrated view of work products extracted from different sources in the enterprise, providing “just in times” knowledge to the user either through explicit search capabilities or through intelligence-based alerting that notifies users of new knowledge, is proposed. Our proposed KMS architecture seeks to facilitate a knowledge sharing environment by stressing ease of retrieval through natural language querying and proactive notification. The KMS should be designed to present the most relevant knowledge to the user. With knowledge stored in a variety of formats and on different technical platforms, it is imperative that a tool be provided that will be able to integrate and correlate knowledge from different sources and enable ease of extraction.

Rate control plays an important role in transmitting low-delay and high-quality images over the channel of very low bandwidth. The rate control algorithm in MPEG-4 or H.26X only defined the rate control model of P-frame, and did not introduce the rate control model of I-frame as it supposed that only the first frame is an I-frame, the others are all P-frames. However, in practical applications, a certain number of I-frames have to be inserted to meet the demand for fault-tolerant transmission and information retrieval. Oriented toward the application demand of remote surveillance system based on PSTN, a rate control strategy is proposed for encoding video at even low bit-rate in this paper. The strategy includes a rate-control algorithm for I-frame and introduces a quadratic rate distortion model, which solves the problem of the rate control used at very low bit-rate preferably. The rate distortion model of I-frame is designed referring to that of P-frame. It mainly involves three processes: initial quantization parameter calculation, objective bit number allocation, and objective quantization parameter calculation.

TM5 of MPEG-2 proposed the target bit-number allocation model of I, P, and B frame, but the algorithm for the model is a bit complex. Based on the quadratic rate distortion model, a novel algorithm for the target bit-number allocation is presented in this paper.

In order to keep the stability of image quality, VM8 limits the change range of the new quantization parameter from −0.25 to 0.25 times of the previous frame. As a result, if the previous frame's quantization parameter is small but the current frame's quantization parameter is calculated to be large, the current frame's quantization parameter should have been corrected with a small value, which will cause excessive coding bits, and worsen the transmission delay. In this paper, we present a correction algorithm for the computation of P-frame's quantization parameter.

The results of experiments show that the proposed scheme reduces the delay of encoder buffering greatly, and improves the quality of reconstructed images clearly at the same time. In terms of the system's transmission delay, our scheme uses reasonable algorithms for computing the quantization parameters of I-frame and P-frame, and adjusting the GOP length dynamically. The transmission delays are significantly decreased and the buffer overflows are avoided. In terms of the reconstructed image quality, the test sequence's PSNRs with our scheme are higher than VM8's at all the bit rates, and the coded bit-number of our scheme is less than VM8's at the same bit rate. In terms of the deviation of output bit-streams, the average deviation of our algorithm is much less than VM8's. The less the deviation of the output bit-stream is, the more appropriate it is to be transmitted at the given bandwidth, and the less the transmission delay is.

With the development of P2P streaming technology, various live streaming systems have been designed. Some measurement studies recently have shown that the most important issue exposed by previous systems is how to balance the benefits between the internet service provider, the internet content provider, and P2P users. This issue is induced by little consideration on management and control. Other problems include how to resolve peers churn and make full use of network bandwidth and apply incentive mechanism among peers.

In this work we introduce AHLSS, a P2P system for live streaming which is hierarchical, adaptive, and extendable. We present the architecture of AHLSS which is divided into three layers. The top layer named AP (Administrator Peer) overlay consisted of some authentic servers, which are arranged by ICP (Internet Content Provider) to provide enrollment and authentication for peers to join P2P overlay and start to watch a media channel. Every AP locates in an alliance alone. The middle layer is SP overlay. AHLSS divides peers that participate in data transmission into two classes, including SP and NP, which base on the capability of peers. SP is elected by AP. SP has responsibility to route media packets quickly and reply peers' channel request within the cluster. The under layer consists of many clusters. A cluster is formed by uniting peers which have poor capability and little stability near SP. SP is named cluster-head with the responsibility for management of members in the cluster.

Our system's innovative features are the following:

It provides the function of enrollment and authentication;

In this paper, we describe in detail the main algorithms that stand behind the design of AHLSS and present the analysis of architecture for various aspects including adaptability, scalability, efficiency, and robustness.

The phenomenal success and popularity of peer-to-peer (p2p) streaming over the last decade took many providers, researchers, as well as the mass, by surprise. It would become the dominant type of traffic over the Internet in the very near future. The released Windows Vista contains its own P2P networking system. Though P2P streaming has become one of the most attractive network applications, it is still awaiting some technological breakthroughs. P2P streaming faces very different challenges from other P2P applications, such as file download. In live P2P streaming, the stringent real-time performance requirement in terms of bandwidth and latency is one of the challenges it faces. In addition, frequent joining and leaving of the autonomous end-hosts have more significant impact on the system performance. So the churn problem and reconstruction due to uncertainty are key challenges of P2P streaming. A number of solutions were proposed which did not take all the factors into account. This paper presents IAM (IP Address Matching), a novel protocol for P2P live streaming system.

For overlay structure, neighbor selection algorithm-FAMM (Forward Address Maximum Matching), which allows the peer choose the close peers as neighbors, is proposed. In this mechanism peer selects neighbors by matching IP address in regular expression. IP address is assigned by area basically, so the closer the peers are, the more the same bits of IP address they have. Peer chooses the first m (m > 0) peers whose IP address has same bits with it as the neighbors. Meanwhile, the latency is generally low. FAMM just likes the DHT routing.

As managing peer failures and proper recovery are of critical importance for a robust P2P streaming system, a new peer exception mechanism is also proposed which is the other central issue of the IAM. According to the different error information which the peer receives, it detects the departure or failure of the neighbor intelligently and recovers from an unstable state promptly.

To evaluate the performance of the IAM, we have conducted extensive simulations and real-network experiment. The results indicate that IAM shortens the latency and is robust to the dynamic peers. It achieves superior comprehensive performance over Random scheme. It is proved effective in the real-network environment, and the latency of recovery is shortened significantly which is only 25% of that with the usual mechanism, as well.

In a traditional mobile streaming system, all users pull streams from the server. This model would cause users a high communication cost and heavy burden to the base station. At the same time, the low bandwidth of the cellular network cannot meet the requirements of high quality video, which greatly limits the large-scale deployment of the mobile multimedia streaming service.

Intelligent mobile phones equipped with a camera are very popular in our daily lives now. Face verification running on mobile phone provides not only a tool for the protection of the owner's authority but also an approach for verification from a distance. The combination of biometrics and telecommunication technologies possesses broad application potentials in information exchanges. However, there are also some critical issues needing solving. Usually the processing power and memory size of a mobile phone system are limited, and the acquisition images are suffered from the illumination changes.

An acquisition image I(x,y) can be modeled as a product of the reflectance R and Illuminant effect L shown as I(x,y) = R(x,y)∗L(x,y). The reflectance attribution R(x,y) is the inherent feature of a human face for verification. Compared with the reflectance changes rapidly across the contour of a face, usually the illumination changes smoothly and slowly. If two pixels are located nearby, the ratio of the reflectance between these two pixels can be calculated via the ratio of their illuminations, which is approximately independent from illumination changes. Based on this invariant property of the ratio of illumination at a pixel to its surrounding neighbor illuminations, an illumination normalization technique was developed and adapted to the mobile system.

A dedicated scheme composed of special wavelets called Gab features was proposed for representation of human faces. Gabor features reveal more structure information of images; however, the data dimensionality is becoming very high. An optimization processing was designed for searching the optimal Gabor node configuration and reducing the number of Gabor nodes effectively.

To fit the limitation of the phone processor, all the floating point calculations were implemented with integer calculations, the feature extraction and discriminating verification algorithms were carefully designed to accelerate implementation and control the heap and stack sizes. A mobile based face verification system was developed and embedded in the Lenovo ET980 mobile phone, which is equipped with CPU Intel XScale PX270, memory 128MB Flash/64 MB SDRAM, and a 4 million pixel camera. The experiments testing on FERET fafb dataset show that the system Equal False Accept Rate and False Reject Rate achieved 1.51%; and the False Accept Rates achieved 2.12% and 6.81%, while the False Reject Rate at 1% and 0.1% respectively. The processing time is about 1 second. The performance of this system is quite well.

Data aggregations from Sensors to a sink in wireless sensor networks (WSNs) are typically characterized by correlation along the spatial, semantic, and temporal dimensions. Exploiting such correlation when performing data aggregation can result in considerable improvements in the bandwidth and energy performance of WSNs. For the sensors-to-sink data delivery, we first explore two theoretical solutions: the shortest path tree (SPT) and the minimum spanning tree (MST) approaches. To approximate the optimal solution (MST) in case of perfect correlation among data, we propose a new aggregation which combines the minimum dominating set (MDS) with the shortest path tree (SPT) in order to aggregate correlated data. To reduce the redundancy among correlated data and simplify the synchronization among transmission, the proposed aggregation takes two stages: local aggregation among sensors around a node in the MDS and global aggregation among sensors in the MDS. Finally, using discrete event simulations, we show that the proposed aggregation outperforms the SPT and closely approximates the centralized optimal solution, the MST, with less amount of overhead and in a decentralized fashion.