Processing In Sensor Networks Research Articles

Enabling Decentralized Privacy Preserving Data Processing in Sensor Networks

Enabling Decentralized Privacy Preserving Data Processing in Sensor Networks

A Dynamic Framework for Internet-Based Network Time Protocol.

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary.

Optimal Scale-Free Small-World Graphs with Minimum Scaling of Cover Time

The cover time of random walks on a graph has found wide practical applications in different fields of computer science, such as crawling and searching on the World Wide Web and query processing in sensor networks, with the application effects dependent on the behavior of the cover time: the smaller the cover time, the better the application performance. It was proved that over all graphs withNnodes, complete graphs have the minimum cover timeNlogN. However, complete graphs cannot mimic real-world networks with small average degree and scale-free small-world properties, for which the cover time has not been examined carefully, and its behavior is still not well understood. In this article, we first experimentally evaluate the cover time for various real-world networks with scale-free small-world properties, which scales asNlogN. To better understand the behavior of the cover time for real-world networks, we then study the cover time of three scale-free small-world model networks by using the connection between cover time and resistance diameter. For all the three networks, their cover time also behaves asNlogN. This work indicates that sparse networks with scale-free and small-world topology are favorable architectures with optimal scaling of cover time. Our results deepen understanding the behavior of cover time in real-world networks with scale-free small-world structure, and have potential implications in the design of efficient algorithms related to cover time.

A Dynamic Fuzzy Engine for Adaptive Control towards improvement of Network Performance in Big Data Environment

With increasing computational utilities in recent times, the need for systems capable of processing immense and voluminous amount of information has witnessed an exponential rise. Concepts of cloud computing and Big Data have proved to be promising solutions to the emerging problems of handling huge volume of data. Big data finds utilities over a wide range of applications starting from sensor networks, data processing involving image acquisition of high-definition data (HD), predictor models involving huge volumes of historical data, classification models involving sentimental analysis, emotion analysis etc. A classic case of big data processing in sensor networks for an IoT environment has been investigated in this research paper with the sole objective of tuning the network performance using a dynamic fuzzy based model. The experimentation has been conducted on a wearable technology database based on IoT comprising of several sensors incorporated in a WBAN (Wireless body area network) technology. Superior performance is reported in the form of improved computation time, accuracy, precision when compared with the big data model without fuzzy engine.

Hierarchical Data Transmission and Processing Analysis of Sensor Network System

Hierarchical structure is the method and means of data transmission in sensor network system. The large-scale application of sensor network system and a large number of data acquisition will produce massive data. In order to reduce the load of sensor network processor system and make data transmission more reliable and convenient. For the transmission and processing of streaming and structured data, the data can be processed hierarchically and dimensionally. Hierarchical processing can effectively reduce the load of the network system; Dimensionality reduction can effectively compress the amount of data to reduce the time of data transmission and processing. Hierarchical operation is a necessary step for data transmission and processing in sensor networks, and has been applied in large-scale data transmission and processing algorithms.

Research on Data Release and Location Monitoring Technology of Sensor Network Based on Internet of Things

The Internet of Things is another information technology revolution and industrial wave after computer, Internet and mobile communication. It is becoming a key foundation and an important engine for the green, intelligent and sustainable development of economic society. The new networked intelligent production mode characterized by the integration innovation of the Internet of Things is shaping the core competitiveness of the future manufacturing industry. The application of sensor network data positioning and monitoring technology based on the Internet of Things in industry, power and other industries is a hot field for the development of the Internet of Things. Sensor network processing and industrial applications are becoming increasingly complex, and new features have appeared in the sensor network scale and infrastructure in these fields. Therefore, the Internet of Things perception data processing has become a research hotspot in the deep integration process between industry and the Internet of Things. This paper deeply analyzes and summarizes the characteristics of sensor network perception data under the new trend of the Internet of Things as well as the research on location monitoring technology, and makes in-depth exploration from the release and location monitoring of sensor network perception data of the Internet of Things. Sensor network technology integrated sensor technology, micro-electromechanical system technology, wireless communication technology, embedded computing technology and distributed information processing technology in one, with easy layout, easy control, low power consumption, flexible communication, low cost and other characteristics. Therefore, based on the release and location monitoring technologies of sensor network data based on the Internet of Things in different applications, this paper studies the corresponding networking technologies, energy management, data management and fusion methods. Standardization system in wireless sensor network low cost, and convenient data management needs, design the iot oriented middleware, and develops the software and hardware system, the application demonstration, the results show that the design of wireless sensor network based on iot data monitoring and positioning technology is better meet the application requirements, fine convenient integration of software and hardware, and standardized requirements and suitable for promotion.

Finite‐time distributed block‐decomposed information filter for nonlinear systems with colored measurement noise

AbstractThis paper considers the distributed filtering problem for discrete‐time nonlinear systems with colored measurement noise obeying a nonlinear autoregressive process in sensor networks. A novel block‐decomposed information‐type filter for such systems is proposed in a centralized fusion structure, by using the statistical linear regression to deal with model nonlinearities and the measurement difference approach to overcome the noise correlation caused by colored measurement noises. Meanwhile, with the help of block matrix inverse operation to realize the high‐dimensional block matrix decomposition, the information vector and information matrix of the original system state in the designed information filter is directly estimated recursively, so as to own good numerical stability. Then, the finite‐time distributed implementation of the proposed filter is put forward, where the final filtering estimate in each sensor node is consistent with the centralized filtering result, by ensuring that each sensor node directly obtains the average values of shared variables in the sensor network through finite iterations of average consensus. Finally, the posterior Cramér–Rao lower bound is derived to show the proposed filter reaches the optimal theoretical performance bound in the premise of statistical linear regression and Gaussian posterior probability density approximation. A target tracking example with colored measurement noise obeying a linear and a nonlinear autoregressive processes validates the proposed method.

PM-LPDR: a prediction model for lost packets based on data reconstruction on lossy links in sensor networks

During the data gathering process in sensor networks, lots of transmitted data packets can be lost due to the limiting node energy and the influence of data redundancy. In order to solve this problem, a prediction model is proposed for lost packets based on data reconstruction on lossy links in sensor networks. In this model, retransmission is adopted for data recovery when a random packet loss is predicted while prediction algorithms based on time sequences can be employed for data recovery when a random packet loss cannot be predicted. The operation of the whole system can still be guaranteed with this model when the packet loss probability of the network is lower than 15% while the relative error for data reconstruction remains between 0.17%~0.22% when the packet loss probability is higher than 22%. It is thus proven that this prediction model exhibits a strong stability and flexibility.

PM-LPDR: a prediction model for lost packets based on data reconstruction on lossy links in sensor networks

During the data gathering process in sensor networks, lots of transmitted data packets can be lost due to the limiting node energy and the influence of data redundancy. In order to solve this problem, a prediction model is proposed for lost packets based on data reconstruction on lossy links in sensor networks. In this model, retransmission is adopted for data recovery when a random packet loss is predicted while prediction algorithms based on time sequences can be employed for data recovery when a random packet loss cannot be predicted. The operation of the whole system can still be guaranteed with this model when the packet loss probability of the network is lower than 15% while the relative error for data reconstruction remains between 0.17%~0.22% when the packet loss probability is higher than 22%. It is thus proven that this prediction model exhibits a strong stability and flexibility.

Security architectures in wireless sensor network

Sensor networks are an apt amalgamation of sensing, processing and communication. These networks are formed using tiny inexpensive sensor nodes which have the attributes of low processing power and short radio ranges. They steer themselves to numerous applications and at the same instance, encounter stark energy constraints and face scarce memory resources. The discerning personas of sensor networks also have a direct influence on the hardware design of these nodes. Innumerable hardware platforms like Crossbow, Intel, and IvMote have been fabricated to examine the ideas of the research community and to implement applications in all arenas of science and technology. With so many applications designed in this area, security issues are of paramount importance. In this article, security architectures on the two layers of the network stack—link layer and network layer have been surveyed. The paper surveys the various security requirements for designing a wireless sensor network discussing the architecture of the protocols in comparison with the security primitives of IEEE 802.15.4 standard for wireless sensor network (Shon and Choi in Towards the implementation of reliable data transmission for 802.15.4-based wireless sensor. Ubiquitous intelligence and computing, 5061. Lecture notes in computer science, 363–372, 2008). The paper examines TinySec (Karlof et al. in TinySec: a link layer security architecture for wireless sensor networks. In: Proceedings of the 2nd international conference on embedded networked sensor systems, 2004) a standard protocol used in TinyOS; the operating system deployed in sensor networks; and C-Sec (Moh’d et al. in C-Sec: energy efficient link layer encryption protocol for wireless sensor networks. In: Proceedings in consumer communications and networking conference (CCNC), 2012) at the link layer and MiniSec (Luk et al. in iniSec: a secure sensor network communication architecture. In: Proceedings of the 6th international conference on information processing in sensor networks (IPSN ‘07), New York, 2007) and MoteSec-Aware (Tsou et al. in IEEE Trans Wirel Commun 12(6):2817–2829, 2013) at the network layer. The survey focusses on the vulnerabilities dealt by each protocol, their challenges with respect to energy consumption, scalability and availability. It also highlights the challenges that the protocols have not been able to handle because of resource restrictions. It also projects some future research directions in the area of security.

Exponential Stability of LMS-Based Distributed Adaptive Filters

In this work, we consider a class of distributed adaptive filters based on the standard least mean squares (LMS) algorithm, which is proposed to track an unknown signal process in sensor networks. We analyze the stability by introducing a stochastic cooperative information (SCI) condition, in the case of non-independent, non-stationary and possibly unbounded signals. Under the SCI condition, the distributed adaptive filters based on the standard LMS will be shown to be able to track a dynamic process of interest from noisy measurements by a set of sensors working collaboratively, in the natural scenario where any sensor cannot fulfill the estimation task individually.

Distributed Density Estimation Based on a Mixture of Factor Analyzers in a Sensor Network.

Distributed density estimation in sensor networks has received much attention due to its broad applicability. When encountering high-dimensional observations, a mixture of factor analyzers (MFA) is taken to replace mixture of Gaussians for describing the distributions of observations. In this paper, we study distributed density estimation based on a mixture of factor analyzers. Existing estimation algorithms of the MFA are for the centralized case, which are not suitable for distributed processing in sensor networks. We present distributed density estimation algorithms for the MFA and its extension, the mixture of Student’s t-factor analyzers (MtFA). We first define an objective function as the linear combination of local log-likelihoods. Then, we give the derivation process of the distributed estimation algorithms for the MFA and MtFA in details, respectively. In these algorithms, the local sufficient statistics (LSS) are calculated at first and diffused. Then, each node performs a linear combination of the received LSS from nodes in its neighborhood to obtain the combined sufficient statistics (CSS). Parameters of the MFA and the MtFA can be obtained by using the CSS. Finally, we evaluate the performance of these algorithms by numerical simulations and application example. Experimental results validate the promising performance of the proposed algorithms.

Resource-Constrained Signal Processing in Sensor Networks

Resource-Constrained Signal Processing in Sensor Networks

Efficient Iceberg Query Processing in Sensor Networks

Efficient Iceberg Query Processing in Sensor Networks

A General Framework for Efficient Continuous Multidimensional Top-k Query Processing in Sensor Networks

Top-k query has long been a crucial problem in multiple fields of computer science, such as data processing and information retrieval. In emerging cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, users may request different sets of information, with different priorities and at different times. Thus, top-k search should not only be multidimensional, but also be across time domain. From the system's perspective, data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resource constrained and system efficiency is of paramount importance. In this paper, we develop a framework that can effectively satisfy demands from the two aspects. The sensor network maintains an efficient dominant graph data structure for data readings. A simple top-k extraction algorithm is used for user query processing and two schemes are proposed to further reduce communication cost. Our methods can be used for top-k query with any linear convex query function. The framework is adaptive enough to incorporate some advanced features; for example, we show how approximate queries and data aging can be applied. To the best of our knowledge, this is the first work for continuous multidimensional top-k query processing in sensor networks. Simulation results show that our schemes can reduce the total communication cost by up to 90 percent, compared with a centralized scheme or a straightforward extension from previous top-k algorithm on 1D sensor data.

Efficient information valuation and costing for distributed wireless sensor networks

Wireless sensor networks (WSNs) often employ complex techniques and cross-layer optimisations to ensure energy efficiency and robustness in WSNs. In this work, the authors present a simple technique of information valuation and costing to utilise bandwidth and energy resources of co-operative WSNs efficiently. This system allows distributed wireless sensor nodes that are operating independently of each other to assess their information content and pre-assign a single-value parameter to the data value before transmission. This parameter is then compared with a single-cost parameter to assess whether it is of benefit for the network to receive the message. The authors show that this is an efficient process in sensor networks, which have common sensing parameters. The process shows that the system can be used in monitoring correlated sources in distributed WSNs.

DHC: Distributed, Hierarchical Clustering in Sensor Networks

In many sensor network applications, it is essential to get the data distribution of the attribute value over the network. Such data distribution can be got through clustering, which partitions the network into contiguous regions, each of which contains sensor nodes of a range of similar readings. This paper proposes a method named Distributed, Hierarchical Clustering (DHC) for online data analysis and mining in senior networks. Different from the acquisition and aggregation of raw sensory data, DHC clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. We also design and evaluate the maintenance mechanisms for DHC to make it be able to work on evolving data. Our simulation results on real world datasets as well as synthetic datasets show the effectiveness and efficiency of our approach.

On Efficient Processing of Continuous Historical Top- $k$ Queries in Sensor Networks

The top-k query has long been an important topic in computer science. Efficient implementation of top-k queries is the key for information searching. In this paper, we develop the Efficient algorithm for the Continuous Historical Top-k (ECHT) extraction, which is a novel algorithm that can effectively process the continuous historical top-k query. A simple top-k extraction algorithm based on aggregation is used for user query processing, and two additional steps on the filter setting by which individual nodes do not have to report all their readings are proposed to further reduce communication cost. To the best of our knowledge, this is the first work for continuous historical top-k query processing in sensor networks, and our simulation results show that our schemes can reduce the total communication cost by up to two orders of magnitude, as compared with the centralized scheme or a straightforward extension from the previous top-k algorithm on a continuous monitoring query.

Query processing in sensor networks

Query processing in sensor networks

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.