Improving data quality using a cross layer protocol in wireless sensor networks

Wireless sensor networks (WSNs) in which the location of sensors is a key information are becoming more and more important. Moreover, in many cases privacy is a key issues for such network which can be the target of different kind of security attacks. In this paper we present an approach, named Cross Layer Protocol (CLP), for improving data quality based on an integrated solution that considers a sound privacy management policy coupled with a secure localization protocol. More specifically, CLP exploits consistency between the information on nodes behavior gathered during localization phase and privacy compliance verification to evaluate nodes reputation. Finally CLP effectiveness is evaluated by means of a set of simulations.     

A survey on sensor localization

Localization is one of the fundamental problems in wireless sensor networks (WSNs), since locations of the sensor nodes are critical to both network operations and most application level tasks. Although the GPS based localization schemes can be used to determine node locations within a few meters, the cost of GPS devices and non-availability of GPS signals in confined environments prevent their use in large scale sensor networks. There exists an extensive body of research that aims at obtaining locations as well as spatial relations of nodes in WSNs without requiring specialized hardware and/or employing only a limited number of anchors that are aware of their own locations. In this paper, we present a comprehensive survey on sensor localization in WSNs covering motivations, problem formulations, solution approaches and performance summary. Future research issues will also be discussed.     

A two-objective memetic approach for the node localization problem in wireless sensor networks

Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then utilized for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes.     

On the interaction between localization and location verification for wireless sensor networks

Secure wireless sensor networks (WSNs) must be able to associate a set of reported data with a valid location. Many algorithms exist for the localization service that determines a WSN node’s location, and current research is developing for location verification, where the network must determine whether or not a node’s claimed location is valid (or invalid). However, the interaction of these two services creates another challenge, since there is no method to distinguish between benign errors, e.g., errors that are inherent to the localization technique, and malicious errors, e.g., errors due to a node’s deceptive location report. In this paper, we study the problem of inherent localization errors and their impact on the location verification service. We propose a localization and location verification (LLV) server model, and define categories of LLV schemes for discrete and continuous resolution. We then designate two metrics to measure the impact of inherent localization errors—the probability of verification (for the discrete location verification schemes) and the CDF of the deviation distance (for the continuous location verification schemes)—to analyze the performance of each LLV category. Numerical results show that a proper tuning mechanism is needed to tolerate even small inherited estimation errors, otherwise the location verification can result in the rejection of almost all nodes. In addition, we propose several location verification feedback (LV-FEED) algorithms to improve the localization accuracy. Analysis of these algorithms shows that a significant improvement in localization accuracy can be accomplished in a few iterations of executing the location verification feedback schemes.     

A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks

Wireless sensor networks (WSNs) with nodes spreading in a target area have abilities of sensing, computing, and communication. Since the GPS device is expensive, we used a small number of fixed anchor nodes that are aware of their locations to help estimate the locations of sensor nodes in WSNs. To efficiently route sensed data to the destination (the server), identifying the location of each sensor node can be of great help. We adopted a range-free color-theory based dynamic localization (CDL) [Shen-Hai Shee, Kuochen Wang, I.L. Hsieh, Color-theory-based dynamic localization in mobile wireless sensor networks, in: Proceedings of Workshop on Wireless, Ad Hoc, Sensor Networks, August 2005] approach, to help identify the location of each sensor node. Since sensor nodes are battery-powered, we propose an efficient color-theory-based energy efficient routing (CEER) algorithm to prolong the life time of each sensor node. The uniqueness of our approach is that by comparing the associated RGB values among neighboring nodes, we can efficiently choose a better routing path with energy awareness. Besides, the CEER has no topology hole problem. Simulation results have shown that our CEER algorithm can save up to 50–60% energy than ESDSR [Mohammed Tarique, Kemal E. Tepe, Mohammad Naserian, Energy saving dynamic source routing for ad hoc wireless networks, in: Proceedings of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, April 2005, pp. 305–310] in mobile wireless sensor networks. In addition, the latency per packet of CEER is 50% less than that of ESDSR.     

LAD: Localization anomaly detection for wireless sensor networks

In wireless sensor networks (WSNs), sensors’ locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery (localization) schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the reference information provided by these beacon nodes. Most of the beacon-based localization schemes assume a benign environment, where all beacon nodes are supposed to provide correct reference information. However, when the sensor networks are deployed in a hostile environment, where beacon nodes can be compromised, such an assumption does not hold anymore. In this paper, we propose a general scheme to detect localization anomalies that are caused by adversaries. Our scheme is independent from the localization schemes. We formulate the problem as an anomaly intrusion detection problem, and we propose a number of ways to detect localization anomalies. We have conducted simulations to evaluate the performance of our scheme, including the false positive rates, the detection rates, and the resilience to node compromises.     

Secure localization with attack detection in wireless sensor networks

Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.     

无线传感器网络中的节点位置验证方法

节点位置信息是感测数据的重要上下文信息,节点自定位技术是无线传感器网络的支撑技术之一。在基于信标节点的定位技术中,信标节点位置的可靠性是影响网络服务质量的关键因素。针对信标节点位置漂移和恶意信标节点引起定位精度下降的问题,提出了一种基于信誉模型的分布式轻量级节点位置验证方法(ReputationbasedLocationVerification,RLV),通过建立无线传感器网络中的节点位置信誉模型来识别网络中的不可靠信标节点。仿真结果表明信誉模型能够较好的反映节点的定位精度,RLV算法可以探测出95%以上的不可靠信标节点。     

An efficient range-free localization algorithm for wireless sensor networks

Node positioning is a fundamental problem in applications of wireless sensor networks (WSNs). In this paper, a new range-free algorithm, called spring swarm localization algorithm (SSLA), is proposed for positioning WSNs. To determine the locations of sensor nodes, the proposed algorithm uses network topology information and a small fraction of sensor nodes which know their locations. Numerical simulations show that high positioning accuracy can be obtained by using the algorithm. Some examples are given to...     

Secure and reliable clustering in wireless sensor networks: A critical survey

In the past few years, research interest has been increased towards wireless sensor networks (WSNs) and their application in both the military and civil domains. To support scalability in WSNs and increase network lifetime, nodes are often grouped into disjoint clusters. However, secure and reliable clustering, which is critical in WSNs deployed in hostile environments, has gained modest attention so far or has been limited only to fault tolerance. In this paper, we review the state-of-the-art of clustering protocols in WSNs with special emphasis on security and reliability issues. First, we define a taxonomy of security and reliability for cluster head election and clustering in WSNs. Then, we describe and analyze the most relevant secure and reliable clustering protocols. Finally, we propose countermeasures against typical attacks and show how they improve the discussed protocols.     

无线传感器网络定位技术研究进展

在无线传感器网络（WSNs）中,通过对节点的定位以确定事件发生的位置是WSNs需要具备的基本功能。介绍了WSNs定位技术的国内外研究现状,分析了静态传感器网络定位、移动信标节点定位和移动传感器网络定位的原理及方法,讨论了几种主要算法的优缺点,给出了移动传感器网络定位技术进一步研究的方向与面临的挑战。     

A soft computing approach to localization in wireless sensor networks

In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs). The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes. Soft computing plays a crucial role in both schemes. In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes. The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA). In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN). The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods.     

WSNs中一种基于移动信标检测的定位算法

信标节点在无线传感器网络(WSNs)定位技术中起着重要的作用,它作为参考节点决定着被定位目标的位置。在WSNs的实际环境应用中,信标节点可能会因为各种原因发生移动成为不可靠的信标节点,此时依赖不可靠信标节点来定位的未知节点将可能产生较大的定位误差,甚至失去了利用价值。针对信标节点发生移动的问题,提出了一种定位前期的基于可用信标的移动信标检测(BAB—BMD)方案。在节点定位之前,对定位节点收到的所有信标进行检测,并对移动信标重定位计算其可靠度。然后,依据信标可靠度选择可用信标节点进行定位,即基于可用信标的信标择优(BAB—BOS)算法。实验结果表明:BABBMD具有较好的检测准确度,同时采用BAB—BOS定位算法定位准确度要高于未进行移动信标检测的定位准确度和丢弃移动信标的定位准确度。     

Efficient localization for mobile sensor networks based on constraint rules optimized Monte Carlo method

Wireless sensor networks (WSNs) have been widely used in many fields. The issue of node localization is a fundamental problem in WSNs. And it is the basis and prerequisite for many applications. Due to the mobility of the sensor nodes, it is more challenging to locate nodes in the mobile WSNs than in the static ones. The existing localization schemes for mobile WSNs are almost based on the Sequential Monte Carlo (SMC) localization method. The SMC-based schemes may suffer from low sampling efficiency resulted from a large sampling area, which makes them difficult to achieve high localization accuracy and efficiency. Some schemes try to reduce the sampling area by further employing position relationship with neighbor common nodes, while we have found that the movements of the neighbor beacon nodes have not been fully exploited. Addressing this issue, in this paper, some new constraint rules are developed and some existing constraint rules are optimized with the consideration of the moving distance and direction of neighbor beacons. A series of distance constraint conditions are further created, by which, the scope/size of the sampling area can be further reduced, and the samples can be filtered more accurately. The performance of our algorithm is evaluated by extensive simulation experiments. The simulation results show that the localization error and computation cost of our proposed algorithm are lower than those of the existing ones, even when the speed of the sensor nodes is relative high.     

A robust localization algorithm in wireless sensor networks

Most of the state-of-the-art localization algorithms in wireless sensor networks (WSNs) are vulnerable to various kinds of location attacks, whereas secure localization schemes proposed so far are too complex to apply to power constrained WSNs. This paper provides a distributed robust localization algorithm called Bilateration that employs a unified way to deal with all kinds of location attacks as well as other kinds of information distortion caused by node malfunction or abnormal environmental noise. Bilateration directly calculates two candidate positions for every two heard anchors, and then uses the average of a maximum set of close-by candidate positions as the location estimation. The basic idea behind Bilateration is that candidate positions calculated from reasonable (i.e., error bounded) anchor positions and distance measurements tend to be close to each other, whereas candidate positions calculated from false anchor positions or distance measurements are highly unlikely to be close to each other if false information are not collaborated. By using ilateration instead of classical multilateration to compute location estimation, Bilateration requires much lower computational complexity, yet still retains the same localization accuracy. This paper also evaluates and compares Bilateration with three multilateration-based localization algorithms, and the simulation results show that Bilateration achieves the best comprehensive performance and is more suitable to real wireless sensor networks.     

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, Bat Optimization Algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly Optimization Algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches.     

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and is sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve the localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial–temporal relationships of the targets we track. Spatial relationships are the relative target locations in a group and temporal relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve the localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory.     

无线传感器网络在协同攻击环境中的安全定位研究

在恶意危险的环境中,一些锚节点可能会被攻击者所俘获,然后向网络中发送错误的信息,目的在于阻碍其他节点的准确定位。介绍了无线传感器网络中存在的攻击类型,建立了协同攻击的数学模型,并提出一种能够抵御协同攻击的安全定位算法。仿真结果表明：在受到30%恶意锚节点协同攻击时,即使在攻击强度较大的情况下,该算法依然能对节点进行准确的定位,且平均定位误差不超过2m。     

基于前进跳距期望的异构WSNs非测距定位算法

无线传感网络(WSNs,wireless sensor networks)中传感节点的传输范围直接决定节点的通信区域,对定位精度有直接的影响.为此,针对异构WSNs,提出基于前进跳距期望的非测距定位算法.首先,分析传统推导前进跳距期望(EHP,expected hop progress)方法的不足,并证实了EHP值只依赖锚节点的传输范围是不准确的;然后,采用新方法推导了EHP,并结合泰勒级数展开以及加权最小二乘算法估计未知传感节点位置;最后,以降低误差为目的,迭代修正未知传感节点位置的估计值,从而提高定位精度.仿真结果表明,与传统的非测距定位算法相比,提出的算法的定位精度得到有效提升.     

Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks

In a wireless sensor network (WSNs), probability of node failure rises with increase in number of sensor nodes within the network. The, quality of service (QoS) of WSNs is highly affected by the faulty sensor nodes. If faulty sensor nodes can be detected and reused for network operation, QoS of WSNs can be improved and will be sustainable throughout the monitoring period. The faulty nodes in the deployed WSN are crucial to detect due to its improvisational nature and invisibility of internal running status. Furthermore, most of the traditional fault detection methods in WSNs do not consider the uncertainties that are inherited in the WSN environment during the fault diagnosis period. Resulting traditional fault detection methods suffer from low detection accuracy and poor performance. To address these issues, we propose a fuzzy rule-based faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. In order to overcome uncertainties that are inherited in the WSN environment, a fuzzy logic based method is utilized. Fuzzy interface engine categorizes different nodes according to the chosen membership function and the defuzzifier generates a non-fuzzy control to retrieve the various types of nodes. In addition, we employed a routing scheme that reuses the retrieved faulty nodes during the data routing process. We performed extensive experiments on the proposed scheme using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm in terms of various important performance metrics.