A probabilistic and opportunistic flooding algorithm in wireless sensor networks

In wireless sensor networks, many communication protocols and applications rely on flooding for various networking purposes. Prior efforts focus on how to design efficient flooding algorithms; that is, they seek to achieve full reliability while reducing the number of redundant broadcasting across the network. To achieve efficient flooding, most of the existing protocols try to reduce the number of transmissions, which is decided without considering any online transmission result. In this paper, we propose a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. It seeks to achieve a target reliability required by an application. For this purpose, it makes a given node select only the subset of its one-hop neighbors to rebroadcast the same message. It considers node relations such as link error rates among nodes in selecting eligible neighbors to rebroadcast. The sender controls the number of retransmissions opportunistically by tracking the current status of message reception at its neighbors. Simulation is carried out to reveal that our proposed scheme achieves the given target reliability with less overhead than other flooding algorithms in most cases, thus prolonging the network lifetime.     

Sensor selection strategies for state estimation in energy constrained wireless sensor networks

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central base station. The base station computes an estimate of the process state by means of a Kalman filter. In this paper we assume that, at each time step, only a subset of all sensors are selected to send their observations to the fusion center due to channel capacity constraints or limited energy budget. We propose a multi-step sensor selection strategy to schedule sensors to transmit for the next T steps of time with the goal of minimizing an objective function related to the Kalman filter error covariance matrix. This formulation, in a relaxed convex form, defines an unified framework to solve a large class of optimization problems over energy constrained WSNs. We offer some numerical examples to further illustrate the efficiency of the algorithm.     

Continuous data aggregation and capacity in probabilistic wireless sensor networks

Due to the existence of many probabilistic lossy links in Wireless Sensor Networks (WSNs) (Liu et al., 2010)  [25], it is not practical to study the network capacity issue under the Deterministic Network Model (DNM). A more realistic one is actually the Probabilistic Network Model (PNM). Therefore, we study the Snapshot Data Aggregation (SDA) problem, the Continuous Data Aggregation (CDA) problem, and their achievable capacities for probabilistic WSNs under both the independent and identically distributed (i.i.d.) node distribution model and the Poisson point distribution model in this paper. First, we partition a network into cells and use two vectors to further partition these cells into equivalent color classes. Subsequently, based on the partitioned cells and equivalent color classes, we propose a Cell-based Aggregation Scheduling (CAS) algorithm for the SDA problem in probabilistic WSNs. Theoretical analysis of CAS and the upper bound capacity of the SDA problem show that the achievable capacities of CAS are all order optimal in the worst case, the average case, and the best case. For the CDA problem in probabilistic WSNs, we propose a Level-based Aggregation Scheduling (LAS) algorithm. LAS gathers the aggregation values of continuous snapshots by forming a data aggregation/transmission pipeline on the segments and scheduling all the cell-levels in a cell-level class concurrently. By theoretical analysis of LAS and the upper bound capacity of the CDA problem, we prove that LAS also successfully achieves order optimal capacities in all the cases. The extensive simulation results further validate the effectiveness of CAS and LAS.     

Coverage and connectivity issues in wireless sensor networks: A survey

Sensing coverage and network connectivity are two of the most fundamental problems in wireless sensor networks. Finding an optimal node deployment strategy that would minimize cost, reduce computation and communication overhead, be resilient to node failures, and provide a high degree of coverage with network connectivity is extremely challenging. Coverage and connectivity together can be treated as a measure of quality of service in a sensor network; it tells us how well each point in the region is covered and how accurate is the information gathered by the nodes. Therefore, maximizing coverage as well as maintaining network connectivity using the resource constrained nodes is a non-trivial problem. In this survey article, we present and compare several state-of-the-art algorithms and techniques that aim to address this coverage–connectivity issue.     

Location privacy and resilience in wireless sensor networks querying

Due to the wireless nature of communication in sensor networks, the communication patterns between sensors could be leaked regardless of the adoption of encryption mechanisms—those would just protect the message content. However, communication patterns could provide valuable information to an adversary. For instance, this is the case when sensors reply to a query broadcast by a Base Station (BS); an adversary eavesdropping the communication traffic could realize which sensors are the ones that possibly match the query (that is, the ones that replied). This issue is complicated by the severe resource constrained environment WSNs are subject to, that call for efficient and scalable solutions.In this paper, we have addressed the problem of preserving the location privacy of the sensors of a wireless sensor network when they send a reply to a query broadcast by the BS. In particular, we deal with one of the worst scenarios for privacy: When sensors are queried by a BS to provide the MAX of their stored readings. We provide a probabilistic and scalable protocol to compute the MAX that enjoys the following features: (i) it guarantees the location privacy of the sensors replying to the query; (ii) it is resilient to an active adversary willing to alter the readings sent by the sensors; and, (iii) it allows to trade-off the accuracy of the result with (a small) overhead increase. Finally, extensive simulations support our analysis, showing the quality of our proposal.     

Design and analysis of a multi-candidate selection scheme for greedy routing in wireless sensor networks

Sleep and wake-up scheduling of sensor nodes is an efficient solution to prolong the network lifetime. However, existing scheduling algorithms may significantly decrease the number of active nodes so that the network may be intermittently connected. In such networks, traditional geographic routing protocols are inappropriate to obtain low latency routes due to route discovery and data forwarding latency. In this paper, we propose a novel multi-candidate selection (MCS) scheme for greedy routing that makes the best effort to find minimum latency routes in the sensor networks. In MCS, each source node sends an RREQ to a list of first wake-up forwarder candidates and selects a route with minimum estimated delivery latency based on their replies. The route found by MCS may be longer than that of distance-based greedy forwarding (DGF) (Finn, 1987). Hence, we introduce a latency-adaptive distance-based multi-candidate selection scheme for greedy forwarding to find routes with a small number of hops and acceptable delivery latency. Probabilistic analysis and simulation results demonstrate that MCS increases the routing performance significantly compared with DGF and ODML (Su et al., 2008) in terms of delivery latency.     

A fuzzy-based interleaved multi-hop authentication scheme in wireless sensor networks

In sensor networks, a compromised node can either generate fabricated reports with false votes or inject false votes into real reports, which causes severe damage such as false alarms, energy drain and information loss. An interleaved hop-by-hop authentication (IHA) scheme addresses the former attack by detecting and filtering false reports in a deterministic and hop-by-hop fashion. Unfortunately, in IHA, all en-route nodes must join to verify reports while only a few are necessary to the authentication procedure. In this paper, we propose a fuzzy-based interleaved multi-hop authentication scheme based on IHA. In our scheme, the fuzzy logic system only selects some nodes for verification based on the network characteristics. Moreover, we apply a voting method and a hash-based key assignment mechanism to improve network security. Through performance evaluation, the proposed scheme is found to save up to 13% of the energy consumption and to provide more network protection compared to IHA.     

An energy-efficient protocol for data gathering and aggregation in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks (WSNs). The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirement of applications/users such as sensing area coverage. In this paper, we propose a novel hierarchical clustering protocol (DEEG) for long-lived sensor network. DEEG achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes, the proposed protocol achieves a good performance in terms of network lifetime. DEEG can also handle the energy hetergenous capacities and guarantee that out-network communications always occur in the subregion with high energy reserved. Furthermore, it introduces a simple but efficient approach to cope with the area coverage problem. We evaluate the performance of the proposed protocol using a simple temperature sensing application. Simulation results show that our protocol significantly outperforms LEACH and PEGASIS in terms of network lifetime and the amount of data gathered.

DyDAP: A dynamic data aggregation scheme for privacy aware wireless sensor networks

End-to-end data aggregation, without degrading sensing accuracy, is a very relevant issue in wireless sensor networks (WSN) that can prevent network congestion to occur. Moreover, privacy management requires that anonymity and data integrity are preserved in such networks. Unfortunately, no integrated solutions have been proposed so far, able to tackle both issues in a unified and general environment. To bridge this gap, in this paper we present an approach for dynamic secure end-to-end data aggregation with privacy function, named DyDAP. It has been designed starting from a UML model that encompasses the most important building blocks of a privacy-aware WSN, including aggregation policies. Furthermore, it introduces an original aggregation algorithm that, using a discrete-time control loop, is able to dynamically handle in-network data fusion to reduce the communication load. The performance of the proposed scheme has been verified using computer simulations, showing that DyDAP avoids network congestion and therefore improves WSN estimation accuracy while, at the same time, guaranteeing anonymity and data integrity.     

无线传感器网络中移动传感器的再定位技术

回顾了在传感器网络中引入移动传感器的过程。介绍了移动传感器再定位技术可解决传感器网络中的事件深入感知、传感器失效和非精确投放等问题。详细阐述了现有的移动传感器移动至感兴趣区域深入感知、扩大网络覆盖面积和修补网络覆盖洞三类技术。剖析了现有技术中存在的问题。总结分析了主要解决方法和模型,并对未来研究方向进行了展望。     

A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Wireless sensor networks (WSNs) as one of the key technologies for delivering sensor-related data drive the progress of cyber-physical systems (CPSs) in bridging the gap between the cyber world and the physical world. It is thus desirable to explore how to utilize intelligence properly by developing the effective scheme in WSN to support data sensing and fusion of CPS. This paper intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce the data transmission and maintain the required coverage level of sensors in WSN while guaranteeing the data confidentiality. The proposed scheme is called GM–KRLS, which is featured through the use of grey model (GM), kernel recursive least squares (KRLS), and Blowfish algorithm (BA). During the data sensing and fusion process, GM is responsible for initially predicting the data of next period with a small number of data items, while KRLS is used to make the initial predicted value approximate its true value with high accuracy. The KRLS as an improved kernel machine learning algorithm can adaptively adjust the coefficients with every input, while making the predicted value more close to actual value. And BA is used for data encoding and decoding during the transmission process due to its successful applications across a wide range of domains. Then, the proposed secure data sensing and fusion scheme GM–KRLS can provide high prediction accuracy, low communication, good scalability, and confidentiality. In order to verify the effectiveness and reasonableness of our proposed approach, we conduct simulations on actual data sets that are collected from sensors in the Intel Berkeley research lab. The simulation results have shown that the proposed scheme can significantly reduce redundant transmissions with high prediction accuracy.     

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.     

Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a parallel energy-efficient coverage optimization mechanism to optimize the positions of mobile sensor nodes based on maximum entropy clustering in large-scale wireless sensor networks. According to the models of coverage and energy, stationary nodes are partitioned into clusters by maximum entropy clustering. After identifying the boundary node of each cluster, the sensing area is divided for parallel optimization. A numerical algorithm is adopted to calculate the coverage metric of each cluster, while the lowest cost paths of the inner cluster are used to define the energy metric in which Dijkstra’s algorithm is utilized. Then cluster heads are assigned to perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric where a weight coefficient between the two metrics is employed to achieve a tradeoff between coverage area and energy efficiency. Simulations of the optimization mechanism and a target tracking application verify that coverage performance can be guaranteed by choosing a proper weight coefficient for each cluster and energy efficiency is enhanced by parallel energy-efficient optimization.     

无线传感器网络的覆盖控制

覆盖控制作为无线传感器网络中的一个基本问题,在国内外已经取得了一些研究成果。根据不同的性质,覆盖控制问题可以划分为不同的类型(如,静态覆盖和动态覆盖、确定性覆盖和随机性覆盖)。主要针对静态覆盖(区域覆盖、点覆盖、栅栏覆盖)问题中一些典型算法,分类进行了描述,并比较了它们之间的优缺点,最后,指出了需要进一步的研究工作。     

A wireless sensor networks MAC protocol for real-time applications

Wireless sensor networks (WSN) are designed for data gathering and processing, with particular requirements: low hardware complexity, low energy consumption, special traffic pattern support, scalability, and in some cases, real-time operation. In this paper we present the virtual TDMA for sensors (VTS) MAC protocol, which intends to support the previous features, focusing particularly on real-time operation. VTS adaptively creates a TDMA arrangement with a number of timeslots equal to the actual number of nodes in range. Thus, VTS achieves an optimal throughput performance compared to TDMA protocols with fixed size of frame. The frame is set up and maintained by a distributed procedure, which allows sensors to asynchronously join and leave the frame. In addition, duty cycle is increased or decreased in order to keep latency constant below a given deadline. Therefore, a major advantage of VTS is that it guarantees a bounded latency, which allows soft real-time applications.

Energy-efficient forwarding in wireless sensor networks

For maximizing the energy efficiency in a wireless network, we propose two forwarding schemes termed single-link and multi-link energy-efficient forwarding that tradeoff delivery ratios against energy costs. Multi-link forwarding improves the network performance substantially by addressing multiple receivers at once during the packet forwarding process. If the first forwarding node does not receive a packet correctly, other nodes may act as backup nodes and perform the forwarding instead. By means of mathematical analyses, we derive how the energy efficiency of a forwarding path can be computed and how a forwarding tree is established. Routing cycles are explicitly taken into account and prevented by means of sequence numbers. Simulations and real-world experiments provide a comparison to other reference strategies, showing a superior performance of our forwarding scheme in terms of energy efficiency.     

无线传感器网络的部署

传感器节点的部署是无线传感器网络中的很重要的问题,因为它反映了传感器网络的成本和监视能力。它和定位、跟踪一样,是无线传感器网络中的一个基本的问题。尽管国外已经在这方面开展了一些相关研究,由于不同的应用有不同的部署特点和目标,仍然面临很多挑战。综合大量无线传感器网络部署相关的技术文献和最新研究结果,着重分析无线传感器网络中节点部署问题的重要性、面临的主要挑战、研究的现状以及对已有算法优缺点的剖析,指出下一步的研究方向。以环境监测中部署算法的设计为例,指出关键的考虑因素。     

McTorrent: Using multiple communication channels for efficient bulk data dissemination in wireless sensor networks

This paper presents McTorrent, a reliable bulk data dissemination protocol for sensor networks. The protocol is designed to take advantage of multiple radio channels to reduce packet collisions and improve the latency of large object dissemination. We evaluated the performance of McTorrent via detailed simulations and experiments based upon an implementation on the TinyOS platform. Our results show that in comparison to Deluge, the de facto network reprogramming protocol for TinyOS, McTorrent significantly reduces the number of packet transmissions and the amount of time required to propagate a large data object through a sensor network.     

一种无线传感器网络的密钥管理机制

介绍了一种用于无线传感器网络(WSNs)的密钥预分配机制:多密钥空间哈希随机密钥预分配(HARPMS)机制。该机制针对group-based节点投放模型,将密钥空间划分成多个子空间,在密钥分配和建立时使用Hash链技术,以提高网络的抗节点俘获能力。分析表明:相比传统的用于group-based投放模型的随机密钥预分配机制,HARPMS获得了同等的连通性,但却有更好的抗节点俘获能力。