trust based data aggregation scheme is secure and it provides both integrity confidentiality and authenticity for homogeneous and heterogeneous networks and this scheme evaluate the malicious behavior of the nodes. These data aggregation schemes provide better security compared with traditional aggregation since cluster heads can directly aggregate the cipher texts without decryption. In this design an encryption and signature techniques are used and also the base station can recover all sensing data even these data has been aggregated.The base station can perform any aggregation function on them. Without compromising any nodes an attacker can interrupt the network system. The hierarchical trust management protocol detects the malicious behavior of the nodes. It is based on four trust components intimacy, honesty, energy, unselfishness of the nodes. It maintains two levels of trust SN-level trust and CH-level trust

several data aggregation schemes based on privacy homomorphism encryption have been designed and reviewed on wireless sensor networks. Cluster heads can exactly aggregate the cipher texts without decryption; thus, transmission overhead is reduced. Though, the base station only fetches the aggregated result, which origin two problems. First, the usage of aggregation function is obliged. Second, the base station cannot confirm the data integrity and authenticity. This paper go to overcome the above two drawbacks. In the design, the base station can recover all the sensing data even the data has been aggregated. Besides, the design has been concluded and adopted on both homogeneous and heterogeneous wireless sensor networks.

sensor networks are energy constrained networks. Energy consumption in these networks can be reduced by processing the raw data at individual nodes through the application of suitable aggregation technique so that there is minimum amount of data that need to be transmitted towards the sink. The data aggregation functions that are applied should adhere to correctness, and should be computationally less complex considering the capabilities of the sensor nodes. In this paper, a brief survey on the present aggregation protocols and their impact, and some of the techniques that are applied at individual sensor nodes to reduce sensed data are presented.

battery is the source of energy for sensors, one of the important issues in wireless sensor networks is the energy and network lifetime. A method to reduce energy consumption and, as a result, increase the network lifetime is the fusion of data collected from the sensors in the covered environment before transmission to wireless sensor network. Data fusion in sensors is defined as the process in which the data received from multiple sources are integrated in order to achieve better perceived information with respect to only one source. In this paper, a new method is proposed for data fusion in network sensors using fuzzy systems. In the proposed method, by integrating the input data into each sensor, each of which had three inputs, the similarity percent of the data in sensors was obtained in order to identify the size of data (packets) to be sent. Simulation results on the proposed method verified the efficiency of the proposed method in terms of energy consumption in the network.

ZENG, WENTE. Secure Distributed Control Methodologies with Built-in Defense in Distributed Networked Control Systems. (Under the direction of Dr. Mo-Yuen Chow.) Distributed Networked Control Systems (D-NCS) are spatially distributed systems that integrate distributed sensors, actuators, and computing processors over a communication network for a vast amount of applications, such as electrical power systems and transportation systems. While most D-NCS have been safe in the past, they are increasingly more vulnerable to malicious cyber attacks and malwares with the rapid advancements and uses with networking, embedded systems, wireless communication technologies, and novel control strategies. In particular, more and more distributed control algorithms are being used in D-NCS because of their flexibility, robustness, computation, and communication features. These algorithms, however, increase the vulnerability of D-NCS to malicious cyber attacks. Thus, there is an urgent growing need to protect control algorithms from malicious cyber attacks in D-NCS. This thesis considers the fundamental task of reaching an agreement (i.e., consensus) among a group of agents via secure distributed computations in D-NCS and discusses the problem of designing secure distributed control methodologies that are capable of performing secure distributed computations in the presence of misbehaving agents. First, we develop the mathematical models of D-NCS and misbehaving agents in the network, and we explore the vulnerabilities of conventional linear consensus algorithms. Second, we propose a reputationbased secure distributed control algorithm with built-in defense mechanism for leaderfollower consensus network. It includes four phases (detection, mitigation, identification, and update) into the control process in a distributed manner and is able to achieve an accurate consensus computation in the presence of misbehaving agents in D-NCS. Third, we extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback recovery and excitation recovery) into the current secure distributed control framework to guarantee the accurate convergence of the well-behaving agents in D-NCS. At each phase, every agent only uses local and one-hop neighbors’ information to identify and isolate the misbehaving agents, and even compensate their effect to the system. Fourth, we develop a trade-off model and corresponding quantitative metrics to address the performance and security trade-off problem of D-NCS. A paradigm of multiagent trade-off optimization based on the Coevolutionary Genetic Algorithm (CGA) is proposed to optimize the trade-off between system real-time performance and security levels. Finally, in order to analyze the performance of our theoretical design on a real-world problem, we examine and validate the effectiveness of the proposed techniques in several illustrative case studies through both simulations and experiments. © Copyright 2013 Wente Zeng All Rights Reserved Secure Distributed Control Methodologies with Built-in Defense in Distributed Networked Control Systems

Wireless sensor networks (WSNs) were originally motivated by military applications, and are becoming integral part of more and more civilian applications to improve quality of life. With current wireless sensor network technology, people will gain advanced knowledge of physical and social systems, and the advent of a ubiquitous sensing era is coming. In-network processing or data aggregation is an essential function of WSNs to collect raw sensory data and get aggregated statistics about the measured environment, and help queriers capture the major feature or changes of the measured systems. As more and more applications of WSNs collect sensitive measurements of people's everyday life, privacy and security concerns draw more and more attention. If privacy of sensory content is not preserved, it is not feasible to deploy the WSNs for information collection. On the other hand, if integrity of the collected sensory information is not protected, no queriers or users can trust and/or use the collected information. Hence, two important issues should be addressed before wireless sensor network systems can realize their promise in civilian applications: (1) protect data privacy, so the deployment of the wireless sensor network systems is feasible; (2) enforce integrity, so users can trust the collected information (or aggregated result). This dissertation explores privacy and integrity of data aggregation in wireless sensor networks. First, I present two privacy-preserving data aggregation schemes for additive aggregation functions, and show that the additive aggregation functions can serve to estimate the aggregation results for more general aggregation functions. The first scheme, Cluster-based Private Data Aggregation (CPDA), leverages clustering protocol and algebraic properties of polynomials. It has the advantage to enable peer monitoring within a cluster. The second scheme, Slice-Mix-AggRegaTe (SMART), builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead for privacy-preserving data aggregation. Then, I address both privacy of individual sensory data and integrity of aggregation result simultaneously. It is very challenging to achieve the synergy of privacy and integrity, because privacy-preserving schemes try to hide or interfere with data, while integrity protection usually needs to enable peer monitoring or public access of the data. Therefore, privacy and integrity can be the conflicting requirements, one may barricade the implementation of the other. I extend SMART and CPDA to preserve privacy and make the queriers able to verify the integrity of data aggregation. To show the efficacy and efficiency of the proposed schemes, I present simulation results of our schemes and compare their performance to a typical data aggregation scheme, Tiny Aggregation protocol (TAG), where no privacy preservation and integrity protection is provided. We explore multiple dimensions in design space, and investigate the tradeoffs in protocol design. To the best of our knowledge, this dissertation is among the first network protocols to preserve privacy and integrity in data aggregation for wireless sensor networks.

Wireless sensor network research usually focuses on the reliable and efficient collection of data. Here, we address the next step in the traces lifetime: we aim at investigating and evaluating, by qualitative and quantitative means, data repositories of already collected measurements. We propose the use of a set of new metrics, which enable reliable evaluation of algorithms using traces (both in average cases and "stressful" setups) removing the need for running algorithms in a real testbed, at least in the development stage.

Wireless ad hoc networks were extensively studied in the past given their potential for scalability, ease of deployment, and suitability for scenarios where no infrastructure is available. Considering the recent relevance of applications, particularly in the Internet of Things (IoT) and edge computing domains, revisiting these networks becomes a necessity, as to develop novel distributed applications. Distributed applications are highly complex as they require multiple services and abstractions supported by a wide range of distributed protocols, specially in such adverse domains. To simplify the development of applications in ad hoc networks, in this paper we present Yggdrasil, a novel framework and middleware specifically tailored for the development and execution of distributed applications and associated protocols using commodity devices in such networks. Yggdrasil provides a simple yet effective development environment, which is achieved by combining an event driven programming model with a multi-threaded execution environment that shield the programmer from concurrency issues. A fully functional prototype was developed in C and experimentally evaluated using a fleet of 24 Raspberry Pis.

Wireless Sensor network has emerged as a promising technique that revolutionizes the way of sensing information. Dense deployed sensor nodes at a specific region are likely to transfer redundant data to the base station. This increase the communicational overhead and affects network lifetime. Since energy conservation is the key issue in WSNs, data aggregation should be incorporated in order to save energy. The main aim of data aggregation technique is to collect and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we present a survey on data aggregation technique in Wireless Sensor Networks (WSN). Here, we explore different types of data aggregation algorithms based on network architecture, approaches and performance characteristics. Based on the study, the future research areas and issues to be focused are Security, Energy conservation, query processing, communication & uncertainty in sensor reading.

Wireless Sensor Networks (WSNs) have emerged as an enabling technology for a variety of distributed applications. WSN middleware eases the development of these applications by providing a uniform programming environment. In this paper we present a rule based approach called REED (Rule Execution and Event Distribution) and describe how it supports flexible programming of WSNs at runtime. Indeed REED is required by the nature of its project setting to allow runtime programming. We demonstrate that by combining this runtime programmability with rules in an event, condition, action format we can support a range of paradigms, including Publish-subscribe and data aggregation algorithms. Current WSN middleware solutions have limited on-line programmability support so the applications cannot re-configure their WSNs while operational. Yet the runtime nature of the prototype requires both the distribution of rules and the events that trigger them so we also describe the rule management approach used to support the rule distribution; in particular a novel rule merging and filtering algorithm is described. The paper reports on the results gained from a REED prototype system constructed in our laboratory using Gumstix.

We study the security challenges that arise in people-centric urban sensing, a new sensor-networking paradigm that leverages humans as part of the sensing infrastructure. Most prior work on sensor networks has focused on collecting and processing ephemeral data about the environment using a static topology and an application-aware infrastructure. People-centric urban sensing, however, involves collecting, storing, processing and fusing large volumes of data related to every-day human activities. Sensing is performed in a highly dynamic and mobile environment, and supports (among other things) pervasive computing applications that are focused on enhancing the user’s experience. In such a setting, where humans are the central focus, there are new challenges for information security; not only because of the complex and dynamic communication patterns, but also because the data originates from sensors that are carried by a person—not a tiny sensor thrown in the forest or mounted on the neck of an animal. In this paper we aim to instigate discussion about this critical issue—because peoplecentric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that

We study the problem of communication scheduling for finite-time average-consensus in arbitrary connected networks. Viewing this consensus problem as a factorization of $\M$ by network-admissible families of matrices, we prove the existence of finite factorizations, provide scheduling algorithms for finite-time average consensus, and derive almost tight lower bounds on the size of the minimal factorization.

We study the problem of aggregate querying over sensor networks where the network topology is continuously evolving. We develop scalable data aggregation techniques that remain efficient and accurate even as nodes move, join or leave the network. We present a novel distributed algorithm called CountTorrent, that enables fast estimation of certain classes of aggregate queries such as COUNT and SUM. CountTorrent does not require a static routing infrastructure, is easily implemented in a distributed setting, and can be used to inform all network nodes of the aggregate query result, instead of just the query initiator as is done in traditional query aggregation schemes. We evaluate its robustness and accuracy compared to previous aggregation approaches through simulations of dynamic and mobile sensor network environments and experiments on micaz motes. We show that in networks where the nodes are stationary, CountTorrent can provide 100% accurate aggregate results even in the presence of lossy links. In mobile sensor networks where the nodes constantly move and hence the network topology changes continuously, CountTorrent provides a close (within 10 -- 20%) estimate of the accurate aggregate query value to all nodes in the network at all times.

We present a novel outlier elimination technique designed for sensor networks. This technique is called RANBAR and it is based on the RANSAC (RANdom SAmple Consensus) paradigm, which is well-known in computer vision and in automated cartography. The RANSAC paradigm gives us a hint on how to instantiate a model if there are a lot of compromised data elements.However,the paradigm does not specify an algorithm and it uses a guess for the number of compromised elements, which is not known in general in real life environments. We developed the RANBAR algorithm following this paradigm and we eliminated the need for the guess. Our RANBAR algorithm is therefore capable to handle a high percent of outlier measurement data by leaning on only one preassumption,namely that the sample is i.i.d. in the unattacked case. We implemented the algorithm in a simulation environment and we used it to filter out outlier elements from a sample before an aggregation procedure. The aggregation function that we used was the average. We show that the algorithm guarantees a small distortion on the output of the aggregator even if almost half of the sample is compromised. Compared to other resilient aggregation algorithms, like the trimmed average and the median, our RANBAR algorithm results in smaller distortion, especially for high attack strengths.

We present LiMoSense, a fault-tolerant live monitoring algorithm for dynamic sensor networks. This is the first asynchronous robust average aggregation algorithm that performs live monitoring, i.e., it constantly obtains a timely and accurate picture of dynamically changing data. LiMoSense uses gossip to dynamically track and aggregate a large collection of ever-changing sensor reads. It overcomes message loss, node failures and recoveries, and dynamic network topology changes. The algorithm uses a novel technique to bound variable size. We present the algorithm and formally prove its correctness. We use simulations to illustrate its ability to quickly react to changes of both the network topology and the sensor reads, and to provide accurate information.

We investigate the geometric properties of the communication graph in realistic low-power wireless networks. In particular, we explore the concept of the curvature of a wireless network via the clustering coefficient. Clustering coefficient analysis is a computationally simplified, semilocal approach, which nevertheless captures such a large-scale feature as congestion in the underlying network. The clustering coefficient concept is applied to three cases of indoor sensor networks, under varying thresholds on the link packet reception rate (PRR). A transition from positive curvature ("meshed" network) to negative curvature ("core concentric" network) is observed by increasing the threshold. Even though this paper deals with network curvature per se, we nevertheless expand on the underlying congestion motivation, propose several new concepts (network inertia and centroid), and finally we argue that greedy routing on a virtual positively curved network achieves load balancing on the physical network.

We discuss the technology detail of data aggregation in wireless sensor networks (WSNs) in this paper, which consists of three parts. First, we apply the classical K-average clustering algorithm to the phase of cluster division which is the first step of the aggregate tree. Second, the cluster head node and the cluster group nodes are constructed based on the entropy formula H(Sg|x)<Fg(c) and pseudo-random function. Finally, we study the secure data fusion method among the nodes.

We develop a distributed second-order proximal algorithm, referred to as SoPro, to address in-network consensus optimization. The proposed SoPro algorithm converges linearly to the exact optimal solution, provided that the global cost function is locally restricted strongly convex. This relaxes the standard global strong convexity condition required by the existing distributed optimization algorithms to establish linear convergence. In addition, we demonstrate that SoPro is computation- and communication-efficient in comparison with the state-of-the-art distributed second-order methods. Finally, extensive simulations illustrate the competitive convergence performance of SoPro.

We describe VarClust, a gossip-based decentralized clustering algorithm designed to support multi-mean decentralized aggregation in energy-constrained wireless sensor networks. We empirically demonstrate that VarClust is at least as accurate as, and requires less node-to-node communication (and hence consumes less energy) than, a state-of-the-art aggregation approach, affinity propagation. This superiority holds for both the clustering and aggregation phases of inference, and is demonstrated over a range of noise levels and for a range of random and small-world graph topologies.

We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a space-time diffusion scheme that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology