Secure encrypted-data aggregation for wireless sensor networks

This paper proposes a secure encrypted-data aggregation scheme for wireless sensor networks. Our design for data aggregation eliminates redundant sensor readings without using encryption and maintains data secrecy and privacy during transmission. Conventional aggregation functions operate when readings are received in plaintext. If readings are encrypted, aggregation requires decryption creating extra overhead and key management issues. In contrast to conventional schemes, our proposed scheme provides security and privacy, and duplicate instances of original readings will be aggregated into a single packet. Our scheme is resilient to known-plaintext attacks, chosen-plaintext attacks, ciphertext-only attacks and man-in-the-middle attacks. Our experiments show that our proposed aggregation method significantly reduces communication overhead and can be practically implemented in on-the-shelf sensor platforms.     

Deterministic binary matrix based compressive data aggregation in big data WSNs

In big data wireless sensor networks, the volume of data sharply increases at an unprecedented rate and the dense deployment of sensor nodes will lead to high spatial-temporal correlation and redundancy of sensors’ readings. Compressive data aggregation may be an indispensable way to eliminate the redundancy. However, the existing compressive data aggregation requires a large number of sensor nodes to take part in each measurement, which may cause heavy load in data transmission. To solve this problem, in this paper, we propose a new compressive data aggregation scheme based on compressive sensing. We apply the deterministic binary matrix based on low density parity check codes as measurement matrix. Each row of the measurement matrix represents a projection process. Owing to the sparsity characteristics of the matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in each projection. Each projection can form an aggregation tree with minimum energy consumption. After all the measurements are collected, the sink node can recover original readings precisely. Simulation results show that our algorithm can efficiently reduce the number of the transmitted packets and the energy consumption of the whole network while reconstructing the original readings accurately.     

Optimal Stochastic Policies for Distributed Data Aggregation in Wireless Sensor Networks

The scenario of distributed data aggregation in wireless sensor networks is considered, where sensors can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. An intrinsic tradeoff between energy and aggregation delay is identified, where nodes must decide optimal instants for forwarding samples. The samples could be from a node's own sensor readings or an aggregation with samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel, a sequential decision process model is proposed to analyze this problem and determine optimal decision policies with local information. It is shown that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit-type policies that are easy to implement in practice. In the case that the required conditions are not satisfied, the performance loss of using the proposed control-limit-type policies is characterized. In general cases, a finite-state approximation is proposed and two on-line algorithms are provided to solve it. Practical distributed data aggregation simulations demonstrate the effectiveness of the developed policies, which also achieve a desired energy-delay tradeoff.     

Malleability Resilient Concealed Data Aggregation in Wireless Sensor Networks

The objective of concealed data aggregation is to achieve the privacy preservation at intermediate nodes while supporting in-network data aggregation. The need for privacy preservation at intermediate nodes and the need for data aggregation at intermediate nodes can be simultaneously realized using privacy homomorphism. Privacy homomorphism processes the encrypted data without decrypting them at intermediate nodes. However, privacy homomorphism is inherently malleable. Although malicious adversaries cannot view transmitted sensor readings, they can manipulate them. Hence, it is a formidable challenge to realize conflicting requirements, such as end-to-end privacy and end-to-end integrity, while performing en route aggregation. In this paper, we propose a malleability resilient concealed data aggregation protocol for protecting the network against active and passive adversaries. In addition, the proposed protocol protects the network against insider and outsider adversaries. The proposed protocol simultaneously realizes the conflicting objectives like privacy at intermediate nodes, end-to-end integrity, replay protection, and en route aggregation. As per our knowledge, the proposed solution is the first that achieves end-to-end security and en route aggregation of reverse multicast traffic in the presence of insider, as well as outsider adversaries.     

A Rate-Distortion Based Aggregation Method Using Spatial Correlation for Wireless Sensor Networks

Today we are witnessing an amazing growth of wireless sensor networks due to many factors including but limited to reducing cost of semiconductor components, rapid deployment of wireless networks, and attention to low-power aspect that makes these networks suitable for energy sensitive applications to a large extent. The power consumption requirement has raised the demand for the new concepts such as data aggregation. Data correlation plays an important role in an efficient aggregation process. This paper introduces a new correlation-based aggregation algorithm called RDAC (Rate Distortion in Aggregation considering Correlation) that works based on centralized source coding. In our method, by collecting correlated data at an aggregation point while using the Rate-Distortion (RD) theory, we can reduce the load of data transmitted to the base station by considering the maximum tolerable distortion by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation in wireless sensor networks. In this paper, a mathematical model followed by implementations demonstrates the efficiency of the proposed method under different conditions. By using the unique features of the RD theory, the correlation matrix and observing the behavior of the proposed method in different network topologies, we can find the mathematical upper and lower bounds for the amount of aggregated data in a randomly distributed sensor network. The bounds not only determine the upper and lower limits of the data compressibility, it also makes possible the estimation of the required bit count of the network without having to invoke the aggregation algorithm. This method therefore, allows us to have a good estimation of the amount of energy consumed by the network.     

An efficient statistical approach for time synchronization in wireless sensor networks

In this paper, we propose a powerful method of estimating the model parameters for time synchronization in wireless sensor networks (WSNs). Joint estimation of clock offset and clock skew has been proposed in the literature using the standard regression framework. Here, we claim that simple regression poorly estimates the parameters because of the inherent correlation among successive time readings between two sensors. We propose an alternative autoregressive model and use generalized least squares for estimating the relative offset and skew parameters. A computationally efficient Bayesian approach is also proposed for the parameter estimation considering correlated readings between two sensors. The effectiveness of the proposed approach compared with the earlier approach has been investigated through extensive simulation studies. Copyright © 2015 John Wiley & Sons, Ltd.     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

MAX–MIN aggregation in wireless sensor networks: mechanism and modeling

In‐network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy‐efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd.     

Routing Correlated Data with Fusion Cost in Wireless Sensor Networks

In this paper, we propose a routing algorithm called minimum fusion Steiner tree (MFST) for energy efficient data gathering with aggregation (fusion) in wireless sensor networks. Different from existing schemes, MFST not only optimizes over the data transmission cost, but also incorporates the cost for data fusion, which can be significant for emerging sensor networks with vectorial data and/or security requirements. By employing a randomized algorithm that allows fusion points to be chosen according to the nodes' data amounts, MFST achieves an approximation ratio of 5/4log(k + 1), where k denotes the number of source nodes, to the optimal solution for extremely general system setups, provided that fusion cost and data aggregation are nondecreasing against the total input data. Consequently, in contrast to algorithms that only excel in full or nonaggregation scenarios without considering fusion cost, MFST can thrive in a wide range of applications     

Adaptive Sensor Fault Detection and Identification Using Particle Filter Algorithms

Sensor fault detection and identification (FDI) is a process of detecting and validating sensor's fault status. Because FDI guarantees system reliable performance, it has received much attention recently. In this paper, we address the problem of online sensor fault identification and validation. For a physical sensor validation system, it contains transitions between sensor normal and faulty states, change of system parameters, and a fusion of noisy readings. A common dynamic state-space model with continuous state variables and observations cannot handle this problem. To circumvent this limitation, we adopt a Markov switch dynamic state-space model to simulate the system: we use discrete-state variables to model sensor states and continuous variables to track the change of the system parameters. Problems in Markov switch dynamic state-space model can be well solved by particle filters, which are popularly used in solving problems in digital communications. Among them, mixture Kalman filter (MKF) and stochastic $M$-algorithm (SMA) have very good performance, both in accuracy and efficiency. In this paper, we plan to incorporate these two algorithms into the sensor validation problem, and compare the effectiveness and complexity of MKF and SMA methods under different situations in the simulation with an existing algorithm---interactive multiple models.      

Scalable decoding on factor trees: a practical solution for wireless sensor networks

We consider the problem of jointly decoding the correlated data picked up and transmitted by the nodes of a large-scale sensor network. Assuming that each sensor node uses a very simple encoder (a scalar quantizer and a modulator), we focus on decoding algorithms that exploit the correlation structure of the sensor data to produce the best possible estimates under the minimum mean-square error (MMSE) criterion. Our analysis shows that a standard implementation of the optimal MMSE decoder is unfeasible for large-scale sensor networks, because its complexity grows exponentially with the number of nodes in the network. Seeking a scalable alternative, we use factor graphs to obtain a simplified model for the correlation structure of the sensor data. This model allows us to use the sum-product decoding algorithm, whose complexity can be made to grow linearly with the size of the network. Considering large sensor networks with arbitrary topologies, we focus on factor trees and give an exact characterization of the decoding complexity, as well as mathematical tools for factorizing Gaussian sources and optimization algorithms for finding optimal factor trees under the Kullback-Leibler criterion.     

A faulty node detection scheme for wireless sensor networks that use data aggregation for transport

This paper presents a faulty node detection approach for wireless sensor networks that aggregate measurement data on their way toward the sink (base station). The approach is based on the idea of commanding sensor nodes on the aggregation paths to temporarily stop including their readings in the received aggregated readings from their upstream neighbors. The scheme is dependent on the ability of the sink to detect faulty nodes through changes in the received aggregated readings at the sink using a Markov Chain Controller (MCC). The algorithm that is run in the sink uses the MCC to assign a state to each sensor node based on transitions that are triggered by receiving aggregated path readings, and accordingly deduces the nodes that may be faulty. The experimental results show at least 98% detection rate at the cost of reasonable detection delays and generated wireless network traffic. Copyright © 2016 John Wiley & Sons, Ltd.     

WSN中一种基于数据融合的能量高效分簇路由协议

基于无线传感器网络中监测数据具有较高时空相关性的应用场景。提出了一种基于数据融合的局部能量高效汇聚分簇协议LEEAC，该协议通过反映局部空间相关性的数据相异度对节点剩余能量进行约束，并使用约束后的预测能量作为竞选簇头的主要依据，被选举的簇头在传感器网络中具有良好的分布性。同时通过引入数据鉴定码，减少了簇内数据传输阶段的通信量以及簇头数据融合的工作量，从而大大节约了能量消耗。实验结果表明，LEEAC协议能够有效均衡网络能量消耗。延长网络生存时间。     

Adaptive data aggregation with probabilistic routing in wireless sensor networks

Periodical extraction of raw sensor readings is one of the most representative and comprehensive applications in Wireless sensor networks. In order to reduce the data redundancy and the communication load, in-network data aggregation is usually applied to merge the packets during the routing process. Aggregation protocols with deterministic routing pre-construct the stationary structure to perform data aggregation. However, the overhead of construction and maintenance always outweighs the benefits of data aggregation under dynamic scenarios. This paper proposes an Adaptive Data Aggregation protocol with Probabilistic Routing for the periodical data collection events. The main idea is to encourage the nodes to use an optimal routing structure for data aggregation with certain probability. The optimal routing structure is defined as a Multi-Objective Steiner Tree, which can be explored and exploited by the routing scheme based on the Ant Colony Optimization and Genetic Algorithm hybrid approach. The probabilistic routing decision ensures the adaptability for some topology transformations. Moreover, by using the prediction model based on the sliding window for future arriving packets, the adaptive timing policy can reduce the transmission delay and can enhance the aggregation probability. Therefore, the packet transmission converges from both spatial and temporal aspects for the data aggregation. Finally, the theoretical analysis and the simulation results validate the feasibility and the high efficiency of the novel protocol when compared with other existing approaches.     

Analog VLSI architectures for motion processing: from fundamentallimits to system applications

This paper discusses some of the fundamental issues in the design of highly parallel, dense, low-power motion sensors in analog VLSI. Since photoreceptor circuits are an integral part of all visual motion sensors, we discuss how the sizing of photosensitive areas can affect the performance of such systems. We review the classic gradient and correlation algorithms and give a survey of analog motion-sensing architectures inspired by them. We calculate how the measurable speed range scales with signal-to-noise ratio (SNR) for a classic Reichardt sensor with a fixed time constant. We show how this speed range may be improved using a nonlinear filter with an adaptive time constant, constructed out of a diode and a capacitor, and present data from a velocity sensor based on such a filter. Finally, we describe how arrays of such velocity sensors call be employed to compute the heading direction of a moving subject and to estimate the time-to-contact between the sensor and a moving object     

Achieving robust wireless localization resilient to signal strength attacks

Received signal strength (RSS) based algorithms have been very attractive for localization since they allow the reuse of existing communication infrastructure and are applicable to many commodity radio technologies. Such algorithms, however, are sensitive to a set of non-cryptographic attacks, where the physical measurement process itself can be corrupted by adversaries. For example, the attacker can perform signal strength attacks by placing an absorbing or reflecting material around a wireless device to modify its RSS readings. In this work, we first formulate the all-around signal strength attacks, where similar attacks are launched towards all landmarks, and experimentally show the feasibility of launching such attacks. We then propose a general principle for designing RSS-based algorithms so that they are robust to all-around signal strength attacks. To evaluate our approach, we adapt a set of representative RSS-based localization algorithms according to our principle. We experiment with both simulated attacks and two sets of real attack scenarios. All the experiments show that our design principle can be applied to a wide spectrum of algorithms to achieve comparable performance with much better robustness.     

Fish bone structure based data aggregation and routing in wireless sensor network: multi-agent based approach

Wireless sensor networks (WSNs) are constrained by limited node (device) energy, low network bandwidth, high communication overhead and latency. Data aggregation alleviates the constraints of WSN. In this paper, we propose a multi-agent based homogeneous temporal data aggregation and routing scheme based on fish bone structure of WSN nodes by employing a set of static and mobile agents. The primary components of fishbone structure are backbone and ribs connected to both sides of a backbone. A backbone connects a sink node and one of the sensor nodes on the boundary of WSN through intermediate sensor nodes. Our aggregation scheme operates in the following steps. (1) Backbone creation and identifying master centers (or nodes) on it by using a mobile agent based on parameters such as Euclidean distance, residual energy, backbone angle and connectivity. (2) Selection of local centers (or nodes) along the rib of a backbone connecting a master center by using a mobile agent. (3) Local aggregation process at local centers by considering nodes along and besides the rib, and delivering to a connected master center. (4) Master aggregation process along the backbone from boundary sensor node to the sink node by using a mobile agent generated by a boundary sensor node. The mobile agent aggregates data at visited master centers and delivers to the sink node. (5) Maintenance of fish bone structure of WSN nodes. The performance of the scheme is simulated in various WSN scenarios to evaluate the effectiveness of the approach by analyzing the performance parameters such as master center selection time, local center selection time, aggregation time, aggregation ratio, number of local and master centers involved in the aggregation process, number of isolated nodes, network lifetime and aggregation energy. We observed that our scheme outperforms zonal based aggregation scheme.     

Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-typically related to proximity-and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio.     

Spatial and Temporal Multi-Aggregation for State-Based Sensor Data in Wireless Sensor Networks

Sensor nodes are thrown to remote environments for deployment and constitute a multi-hop sensor network over a wide range of area. Users hardly have global information on the distribution of sensor nodes. Hence, when users request state-based sensor readings such as temperature and humidity in an arbitrary area, networks may suffer unpredictable heavy traffic. This problem needs data aggregation to comply with user requirements and manage overlapped aggregation trees of multiple users efficiently. In this paper, spatial and temporal multiple aggregation (STMA) is proposed to minimize energy consumption and traffic load when a single or multiple users gather state-based sensor data from varions subareas through multi-hop paths. Spatial aggregation builds the aggregation tree with an optimal intermediary between a target area and a sink. The broadcast nature of wireless communication is exploited to build the aggregation tree in the confined area. Temporal aggregation uses the interval so that users obtain an appropriate amount of data they need without suffering excess traffic. The performance of STMA is evaluated in terras of energy consumption and area-to-sink delay in the simulation based on real parameters of Berkeley's MICA motes.     

Spatial Model for Energy Burden Balancing and Data Fusion in Sensor Networks Detecting Bursty Events

In this paper, we propose a stochastic geometric model to study the energy burdens seen in a large scale hierarchical sensor network. The network makes use of aggregation nodes, for compression, filtering, and/or data fusion of locally sensed data. Aggregation nodes (AGNs) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network, it may have the deleterious effect of concentrating loads on paths between AGNs and the sinks-such inhomogeneities in the energy burden may in turn lead to nodes with depleted energy reserves. To remedy this problem, we consider how one might achieve a more balanced energy burden across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the lifetime for the system. We show that the scale of aggregation and degree of spreading can be optimized. Additionally, if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast, if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.