Editorial

Wireless sensor network(WSN)is characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon.The main advantages of WSN include its low cost,rapid deployment,self-organization,and fault tolerance.WSN has received tremendous interests of various research communities,and significant progresses have been made in various aspects including sensor platform development,wireless communication and networking,signal and information processing,as well as network performance eva...     

SNEE: a query processor for wireless sensor networks

A wireless sensor network (WSN) can be construed as an intelligent, large-scale device for observing and measuring properties of the physical world. In recent years, the database research community has championed the view that if we construe a WSN as a database (i.e., if a significant aspect of its intelligent behavior is that it can execute declaratively-expressed queries), then one can achieve a significant reduction in the cost of engineering the software that implements a data collection program for the WSN while still achieving, through query optimization, very favorable cost:benefit ratios. This paper describes a query processing framework for WSNs that meets many desiderata associated with the view of WSN as databases. The framework is presented in the form of compiler/optimizer, called SNEE, for a continuous declarative query language over sensed data streams, called SNEEql. SNEEql can be shown to meet the expressiveness requirements of a large class of applications. SNEE can be shown to generate effective and efficient query evaluation plans. More specifically, the paper describes the following contributions: (1) a user-level syntax and physical algebra for SNEEql, an expressive continuous query language over WSNs; (2) example concrete algorithms for physical algebraic operators defined in such a way that the task of deriving memory, time and energy analytical cost-estimation models (CEMs) for them becomes straightforward by reduction to a structural traversal of the pseudocode; (3) CEMs for the concrete algorithms alluded to; (4) an architecture for the optimization of SNEEql queries, called SNEE, building on well-established distributed query processing components where possible, but making enhancements or refinements where necessary to accommodate the WSN context; (5) algorithms that instantiate the components in the SNEE architecture, thereby supporting integrated query planning that includes routing, placement and timing; and (6) an empirical performance evaluation of the resulting framework.     

In-network wireless sensor network query processors: State of the art,challenges and future directions

In wireless sensor networks (WSNs), energy is valuable because it is scarce. This causes their life time to be determined by their ability to use the available energy in an effective and frugal manner. In most of the earlier sensor network applications, the main requirement consisted mainly of data collection but transmitting all of the raw data out of the network may be prohibitively expensive (in terms of communication) or impossible at given data collection rates.In the last decade, the use of the database paradigm has emerged as a feasible solution to manage data in a WSN context. There are various sensor network query processors (SNQPs) (implementing in-network declarative query processing) that provide data reduction, aggregation, logging, and auditing facilities. These SNQPs view the wireless sensor network as a distributed database over which declarative query processor can be used to program a WSN application with much less effort. They allow users to pose declarative queries that provide an effective and efficient means to obtain data about the physical environment, as users would not need to be concerned with how sensors are to acquire the data, or how nodes transform and/or transmit the data.This paper surveys novel approaches of handling query processing by the current SNQP literature, the expressiveness of their query language, the support provided by their compiler/optimizer to generate efficient query plans and the kind of queries supported. We introduce the challenges and opportunities of research in the field of in-network sensor network query processing as well as illustrate the current status of research and future research scopes in this field.     

Opportunistic sampling-based query processing in wireless sensor networks

High resolution sampling of physical phenomenon is a prime application of large scale wireless sensor networks (WSNs). With hundreds of nodes deployed over vast tracts of land, monitoring data can now be generated at unprecedented spatio-temporal scales. However, the limited battery life of individual nodes in the network mandates smart ways of collecting this data by maximizing localized processing of information at the node level. In this paper, we propose a WSN query processing method that enhances localized information processing by harnessing the two inherent aspects of WSN communication, i.e., multihop and multipath data transmission. In an active WSN where data collection queries are regularly processed, multihop and multipath routing leads to a situation where a significant proportion of nodes relay and overhear data generated by other nodes in the network. We propose that nodes opportunistically sample this data as they communicate. We model the data communication process in a WSN and show that opportunistic sampling during data communication leads to surprisingly accurate global knowledge at each node. We present an opportunistic query processing system that uses the accumulated global knowledge to limit the data collection requirements for future queries while ensuring temporal freshness of the results.     

MaD‐WiSe: a distributed stream management system for wireless sensor networks

Wireless sensor networks (WSN) are composed of several sensors having limited memory, processing power, communication bandwidth, and energy, which cooperate in performing a given task. The use of the database paradigm has emerged in the last few years as a viable solution to manage data in such a context. In this paper we present the MaD‐WiSe system, a distributed query processing framework that moves the processing of the query into the network. MaD‐WiSe reconsiders various aspects related to database system design and it reinterprets them according to the WSN constraints and requirements. In particular it considers the aspects related to the definition of a query language to formalize the queries, a stream model to manage data acquired by the sensors, a query algebra to define the operators that actually perform the query, and energy efficiency and query optimization strategies for saving energy. Copyright © 2010 John Wiley & Sons, Ltd.     

Duty cycle aware spatial query processing in wireless sensor networks

The recent evolution in sensor node location technology has spurred the development of a special type of in-network processing for wireless sensor networks (WSN), called spatial query processing. These queries require data from nodes within a region (called region of interest) defined by the users. The state of the art of spatial query processing considers, in general, that nodes are always on. However, nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes an energy-efficient in-network spatial query processing mechanism that assumes nodes having no knowledge about their neighbors. The proposed mechanism is able to process spatial queries without the necessity of periodic beacon transmissions for neighbor table updates or for synchronization. Hence, it can work properly over different types of duty cycle algorithms.     

带执行器节点的无线传感器网络的分簇算法

带执行器节点的无线传感器网络(WSAN)是指在无线传感器网络中加入执行器,传感器用于检测物理环境信息,执行器收集和处理这些检测数据,并作出适当的执行任务。传感器和执行器的协作是WSAN研究的一个重要内容,就此提出了一个动态分簇算法,根据事件发生的实际情况,仅仅对该事件区域分簇,每个簇包括一个执行器节点以及传送数据到该执行器节点的传感器节点。通过这种分簇,可以决定传感器与哪个执行器通信以及路由方式。     

A statistical analysis of interference and effective deployment strategies for facility-specific wireless sensor networks

Sensors are essential to industrial automation as they provide vital links between control systems and the physical world. Recently, wireless sensor networks (WSNs) attract more attention as they become feasible solutions for facility management. Unlike simulated environments, however, there are challenges in developing reliable WSNs for monitoring real facilities, including reduced accuracy, reliability and performance due to unpredictable interferences. This paper investigates deployment of automation facility-specific WSNs, called facility sensor networks (FSNs). First, interferences at multiple sensing nodes are analyzed to see if FSNs are vulnerable to interference. Second, interference sources are identified by applying statistical methods to collected data, in order to find the appropriate FSN configuration. Finally, an interference model is proposed to obtain optimal deployment strategies that minimize influence of interference. The strategy yields the lowest interference level compared to others. The results also suggest the appropriate number of sensors to be deployed.     

Instrumenting Wireless Sensor Networks — A survey on the metrics that matter

Wireless Sensor Networks (WSN) are part of the technical fundament enabling the ‘Internet of Things’ (IoT), where sensing and actuator nodes instantaneously interact with the environment at large. As such they become part of everyday life and drive applications as diverse as medical monitoring, smart homes, smart environment, and smart factories, to name but a few. To acquire data, individual sensors interact with the physical environment by sensing physical phenomena in proximity. The wireless network connectivity is leveraged to collect the raw data or pre-processed events, and to disseminate code, queries or commands. Actuating capabilities facilitate instant interactions with the environment or application processes. Experience on how to operate large scale heterogeneous WSNs in (critical) real-world applications is still scarce, and operational considerations are often an afterthought to WSN deployment. A principled look into the metrics, i.e., a standard or best practice of measurement of the ‘vital’ parameters in WSNs is still missing. In this article, we contribute a survey on the most important metrics to characterize the performance of WSNs. We define an abstract system model for WSNs, take a look on what the WSN community considers ‘metrics that matter’, and categorize the metrics into scopes of relevance. We discuss the properties of the metrics as well as practical aspects on how to obtain and process them. Our survey can serve as a ‘manual’ for implementors and operators of WSNs in the IoT.     

Curracurrong: a stream programming environment for wireless sensor networks

The technological advances in wireless sensor network (WSN) enable the development of complex applications including health monitoring, environmental sampling, and disaster area monitoring. WSN applications deploy battery‐powered sensors at remote locations for long periods. The development of energy‐efficient and complex WSN applications therefore requires in‐depth embedded systems programming skills that are normally not found in domain experts. So that this challenge can be overcome, programming environments for WSN need to offer a high degree of productivity, flexibility, and efficiency at the same time. In this work, we present Curracurrong, a development environment for WSNs that is based on expressing queries with stream programming. A query is represented as a stream graph consisting of stream operators and communication channels. Curracurrong provides an extensible stream operator library that adapts to a wide range of applications. It uses a novel placement algorithm that optimizes the energy consumption on sensor nodes. Through a case study, we demonstrate the productivity and flexibility of our system. We conduct experiments that evaluate the energy efficiency of our optimized operator placement algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.     

基于HBase的大规模无线传感网络数据存储系统

无线传感网络(WSN)存在分布的跨区域性,随着无线传感网络的扩张,传感器数目增多,将产生大规模的传感数据。针对存储大规模无线传感网络数据的问题,提出了一个两层分布式存储架构,使用分布式数据库HBase存储跨区域的无线传感网络数据和全局数据存储管理目录,实现一个近实时的存储系统。实验结果证明,该系统有良好的扩展性、存储和查询效率。     

On query processing in wireless sensor networks using classes of quality of queries

This paper introduces the concept of quality of queries (QoQs) towards a more adaptive query processing in wireless sensor networks (WSNs). This approach aims at the intelligent consumption of the limited resources (energy and memory) available in these networks while still delivering a reasonable level of data quality as expected by client applications. In a nutshell, the concept of QoQ stipulates that the results of different queries injected into the same WSN can be tailored according to different criteria, in particular the levels of query result accuracy and energy consumption. For this purpose, four classes of QoQ (CoQoQ) are specified having in mind distinct requirements in terms of these criteria. To allow the implementation of these classes in a real WSN setting, a new novelty-detection based algorithm, referred to as AdaQuali (which stands for “ADAptive QUALIty control for query processing in WSN”), is also proposed in a manner as to control the sensor node activities through the dynamic adjustment of their rates of data collection and transmission. In order to validate the novel approach, simulations with a prototype implemented in Sinalgo have been conducted over real temperature data. The results achieved evidence the suitability of the proposal and point to gains of up to 66.76%, for different CoQoQ, in terms of reduction in energy consumption.     

Combining simulated annealing with Lagrangian relaxation and weighted Dantzig–Wolfe decomposition for integrated design decisions in wireless sensor networks

A Wireless Sensor Network (WSN) is the outcome of the collaborative effort of multi-functional, low-power, low-cost, tiny electronic devices called sensors. Their ability to work autonomously provides a distributed environment capable to monitor even remote or inaccessible areas, which explains the wide application range of WSNs. There are four main issues in the design of a WSN: determining sensor locations (deployment), scheduling sensors, finding sink locations, and obtaining sensor-to-sink data routes. Sensors have very limited energy resources and their efficient management becomes critical for elongating network lifetime. As a result, most of the works on optimal WSN design are concerned with efficient energy usage. Unfortunately, only a few of them use an integrated approach and try to address these four issues simultaneously. In this work we also follow this line of research and develop first a monolithic mixed-integer linear programming model that maximizes network lifetime by optimally determining sensor and sink locations, sensor-to-sink data flows, active and stand-by periods of the sensors subject to data flow conservation, energy consumption and budget usage constraints. Then we propose a nested solution method consisting of two procedures: simulated annealing that performs search for the best sink locations in the outer level and Lagrangian relaxation based heuristic employed with weighted Dantzig–Wolfe decomposition for the multiplier update in the inner level, which determines sensor locations, activity schedules of the sensors and data flows routes. We demonstrate the efficiency and accuracy of the new approach on randomly generated instances by extensive numerical experiments.     

Fault-resilient sensing in wireless sensor networks

Research on wireless sensor networks (WSNs) has received tremendous attention in the past few years due to their potential applications and advances in the VLSI design. In WSNs with tiny sensors, mobility of a sink may provide an energy efficient way for data dissemination. Having a mobile sink in WSN, however, creates new challenges to routing and sensor distribution modeling in the network. In this paper, based on clustering and routing optimization algorithms, we propose a new scheme called K-means and TSP-based mobility (KAT mobility). After clustering the sensor nodes, the proposed method navigates the mobile sink to traverse through the cluster centers according to the trajectory of an optimized route. The mobile sink then collects the data from sensors at the visited clusters. Simulation results have demonstrated that the proposed scheme can provide not only better energy efficiency as compared to those obtained by conventional methods which assume random waypoint for the mobile sink, but also fault-resilience in case of malfunctions of some sensors due to attacks.     

无线传感器网络智能信息处理研究

由大量微小传感器节点组成的无线传感器网络主要用于从目标对象收集信息,但由于节点资源受限,给无线传感器网络的信息处理带来了严峻挑战,为此,必须采取简单、高效的处理策略.本文综述了无线传感器网路环境下智能信息处理的最新研究进展,包括网内聚合、数据压缩和分布式存储和查询等方法,对各种算法的优缺点进行评述,并指出了其关键问题.孙优贤(1940-),男,教授,博士生导师,中国工程院院土,研究方向为复杂系统理论、分布控制系统以及企业综合自动化等.     

Improving the QoS for information discovery in autonomic wireless sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. In order to fully exploit the data gathering and dissemination capabilities of these networks, energy-efficient and scalable solutions for data storage and information discovery are essential. Traditionally, the communication pattern in WSNs has been assumed to be many-to-one; i.e., numerous sensors gather information which is routed to a central point commonly referred to as the sink. However, many emerging applications for WSNs require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery.In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay resulting in quality of service (QoS) improvements. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group “similar” sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time.Simulation and Analytical results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.     

Information extraction from sensor networks using the Watershed transform algorithm

Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%.     

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.     

Distributed optimization in wireless sensor networks: an island-model framework

Wireless sensor networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (distributed optimization for WSN) is an island-model infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of 15 continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.