Data Aggregation

behaviour of the participants up to that moment and has to be robust in the presence of various types of faults and malicious behaviour. There are a number of incentives for attackers to manipulate the trust and reputation scores of participants in a distributed system, and such manipulation severely impairs the performance of such a system. The main target of malicious attackers are aggregation algorithms of trust and reputation systems Trust and reputation have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs) Although sensor networks are being increasingly deployed in many application domains, assessing trustworthiness of reported data from distributed sensors has remained a challenging issues. Sensors deployed in hostile environments may be subject to node compromising attacks by adversaries who intend to inject false data into the system. In this context, assessing the trustworthiness of the collected data and announcing decision makers for the data trustworthiness becomes a challenging task. As the computational power of very low power processors dramatically increases, mostly driven by demands of mobile computing, and as the cost of such technology drops, WSNs will be able to afford hardware which can implement more sophisticated data aggregation and trust assessment algorithms; an example is the recent emergence of multi-core and multi-processor systems in sensor nodes . Aggregation has been used in many different areas of networking and security. Initially it was used for collecting data from multiple sensor nodes and send towards the base station. Later it has been also used for compressed data to return the result in common information of all data. The main reason for this was the noise in the text document.

However, in many applications the security aspects are as important as performance and low energy consumption. The security challenges include the extremely large number of interacting devices in a sensor network and the dynamic nature of WSN, that is, frequent changes in both its topology and its membership. Privacy is the ability of an individual or group to seclude them or information about themselves and thereby reveal who they are selectively. As location tracking capabilities of mobile devices are increasing, problems related to user privacy arise, since user's position and preferences constitute personal information and improper use of them violates user's privacy. Our new private data aggregation protocol is based on a special broadcast communication scheme, where the nodes of the cluster organize themselves into a ring and each data packet to be included in the aggregated result is sent around that ring. Note that a broadcast communication scheme is necessary, because in our scheme the nodes do not know the identity of the aggregators, therefore, they can only send data to the aggregators by broadcasting. We chose the ring based broadcast scheme, because our private query protocol exploits its properties. Our new private query protocol allows the aggregator nodes to respond to the queries of the base station without leaking any information about their identity. For this, a query token is passed around the ring, and each non-aggregator node adds some noise to the token, while the aggregator adds noise and the aggregated result.

In clustered environments, there are some approaches for data aggregation. The first approach is known as the Group Head(GH) method. The idea behind the approach is that One node in the cluster will be selected as the GH based on node connectivity. The remaining nodes in the cluster will Send data to the GH. The GH will collect all the data and will forward these data to the destination node. [3]

The core purpose of data aggregation of the wireless sensor network is to simplify data, eliminate duplicated data, reduce redundant data and traffic to ultimately achieve energy saving and prolong network operation time. The traditional spatial data aggregation algorithms mainly adopt the tree-type network and node cluster topology structures, both of which are root nodes of Sink. They aggregate data through nodes and then transmit the encrypted or disturbed aggregation structures back to Sink. Their shortcoming is high energy consumption. In this paper, we make improvements based on the traditional data aggregation algorithms. Data aggregation mainly carries out node sensing within a certain area based on multiple dynamic routes. The algorithm process is shown in Figure 1.

In this paper Chien-Ming Chen, Yue-Hsun Lin et al [8], introduced a concept named Recoverable Concealed Data Aggregation. In recoverable concealed Data Aggregation, a base station can recover each sensing data generated by all sensors even if these data have been aggregated by cluster heads. With these individual data, two functionalities are provided. First, the base station can verify the integrity and authenticity of all Sensing data. Second, the base station can perform any aggregation functions on them. Then, they propose two recoverable concealed data aggregation schemes named recoverable Concealed Data Aggregation Homogenous and Recoverable Concealed Data Aggregation Heterogeneous WSN respectively, but at the cost of transmission cost i.e. transmitting the sensed data and aggregated data to the base station. The figure 1 shown below gives the idea about the aggregated data and the sensed data to the base station.

Sensor nodes are not usually equipped with tamper-resistant hardware. As a result, an attacker can take control of a sensor node by physically compromising it. Using the compromised nodes, an attacker can easily manipulate the result of aggregation by reporting malicious readings that introduce significant deviations in the aggregated result. For example, a protocol that computes the average of sensor readings is insecure against a single large reading introduced by an attacker. Current data aggregation techniques are not designed with security in mind. Some of the popular aggregation functions such as SUM, AVG, MIN, MAX and COUNT are shown to be insecure in [55]. Apart from node compromises, an attacker can introduce significant deviations in the aggregated results by creating fake events. For example, an adversary can artificially inflate a sensor reading by holding a cigarette lighter in the vicinity of a heat sensor. Cryptographic authentication mechanisms alone cannot solve this problem because an attacker may have access to valid cryptographic keys via compromised nodes, and easily introduce deviating readings into the network. Sensor nodes are also vulnerable to system faults that enable malfunctioning sensors to report outlying data values. We denote all these deviating sensor readings as outliers. It is of utmost importance to detect and eliminate the influence of the outliers on the aggregated result by designing a resilient aggregation scheme that can tolerate some percentage of outliers.

J. Girao, D. Westhoff, and M. Schneider [6] introduce an approach that 1) covers identified data end-to-end by 2) up 'til now providing beneficial and versatile in-network data aggregation. The aggregating mediatory nodes are not important to work at the distinguished plaintext data. They implement a particular class of encrypted changes and discuss frameworks for enlisting the aggregate capacities "average" and "movement detection." They exhibit that the technique is feasible for the class of "going down" routing protocols. They consider the danger of contaminated sensor nodes by proposing a key pre-distribution algorithm that confines an attackers expansion and show up how key pre- distribution and a key-ID sensitive "going down" routing protocol grows the quality and trustworthiness nature of the related spine.

mal readings with no further investigation is impractical, especially in applications designed for heterogeneous environments, such as the monitoring of bush-fires or monitoring temper- atures within oil refineries. In these heterogeneous environments, the normal and abnormal readings are equally important for the network administrator. RSDA is similar to ¨ Ozdemir’s schemes [91, 92] in the sense that it minimizes the use of heavy cryptographic mechanisms, and integrates aggregation functionalities with a reputation system, in order to secure data aggregation. However, the differences between RSDA and ¨ Ozdemir’s scheme are four-fold: (i) RSDA considers the main phases in the analysis framework for reputation systems discussed in Section 3.1, (ii) RSDA considers both WSNs-related and reputation-related security attacks, (iii) RSDA is not limited to a single aggregation function, and (iv) RSDA provides dynamic response to attack activities by not rejecting incorrect aggregation results at the base station level. Instead, it rejects it as soon as possible, possibly by nodes in the neighborhood. We believe that these differences ensure that the main components of our definition for robust secure data aggregation discussed in Section 2.1 are satisfied. The notation to be used in this chapter is found in Table 4.1.

Although it is a long-held belief that SMPCs may be used to generically design differ- entially private data aggregation protocols, such an approach has not been undertaken so far due to the inefficiency of generic constructions. In this work we demonstrated the viability of such an approach, by designing an SMPC architecture that constitutes not only a generic, but also a practical building block for designing a variety of privacy- preserving data aggregation protocols. In particular, the computational effort on the client side is negligible, which makes PrivaDA suitable even for computationally lim- ited devices, such as smartphones. In contrast to previous works, PrivaDA supports a variety of perturbation mechanisms, offers strong privacy guarantees as well as optimal utility, and is resistant to answer pollution attacks. Furthermore, PrivaDA can support a large number of clients without any significant performance penalty.

Data aggregation is an important primitive in wireless sensor networks (WSN). Data aggregation is a process that collects data from different sources and expresses the data in a summarized format. By eliminating redundant or unnecessary information, data aggregation can improve the communication efficiency of a sensor network. A significant risk of data aggregation however is that a node that is captured by an adversary can report arbitrary values as its aggregation result, thereby corrupting not only its own measurements but also that of all the nodes in its entire aggregation sub-tree[6]. As a consequence, an adversary who captures nodes selectively and strategically can corrupt the entire network aggregation process, while incurring minimal cost and effort. Context Summarization (CS) is a method of representing raw context information into summarized information so that it takes relatively less storage space and can successfully answer the queries for complete information with acceptable degree of confidence [7]. Such a compact representation of information reduces required storage space. It has been identified that police patrols are in need of an integrated version of privacy and context summarization. A typical police patrol system comprises of nodes belonging to police officer, police commissioner and the criminal. The wireless sensors in the network are used to record necessary details. The police commissioner plays a key role in ensuring privacy, performing aggregation and has the access to the database.

The rapid advances in processor, memory, and radio technology have enabled the development of distributed networks of small, inexpensive sensor nodes that are capable of sensing, computation, and wireless communication [1], [2], [3], [4]. For ease of deployment, sensor devices should be inexpensive, small, and have a long lifetime, which makes it important to develop very efficient software and hardware solutions. For this reason, protocols for sensor networks should be carefully designed so as to make the most efficient use of the limited resources in terms of energy, computation, and storage. The area of communications and protocol design [6], [9], [41] for sensor networks has been widely researched in the past few years, and many solutions have been proposed and compared. In this survey we focus on an important aspect of sensor network: data aggregation. In-network data aggregation is at the heart of sensor network research which allows trading off communication for computational complexity. For a given application area, network resource constraints, local computation often consumes significantly less energy than communication. In particular, resource efficiency, timely delivery of data [16]-[18], [25] to the sink node, and accuracy of the results [47] are conflicting goals, and the optimal trade-off among them largely depends on the specific application. Data aggregation has been widely recognized as an efficient method to reduce energy consumption in wireless sensor networks, which can support a wide range of applications such as monitoring temperature, humidity, level, speed etc. Section II describes the basic concepts of data aggregation, section III presents the objectives of data aggregation, section IV describes the basic ingredients of In-network data aggregation, section V presents several routing protocols regarding data aggregation, section VI represents data representation and in-network data aggregation function, section VII presents challenges of data

Intanagonwiwat et al. [5] have developed an energy efficient data aggregation protocol called directed diffusion. Directed diffusion is a representative approach of two phase pull diffusion. It is a data centric routing scheme which is based on the data acquired at the sensors. The attributes of the data are utilized message in the network. Figure 1 illustrates the interest propagation in directed diffusion. If the attributes of the data generated by the source match the interest, a gradient is set up to identify the data generated by the sensor nodes. The sink initially broadcasts an interest message in the network. The gradient specifies the data rate and the direction in which to send the data. Intermediate nodes are capable of caching and transforming the data. Each node maintains a data cache which keeps track of recently seen data items. After receiving low data rate events, the sink reinforces one particular neighbor in order to attract higher quality data. Thus, directed diffusion is achieved by using data driven local rules.

ABSTRACT: Wireless sensor networks are vulnerable to many types of security attacks, including data forgery, false data injection and eavesdropping. Sensor nodes can be compromised by intruders and the compromise nodes can distort data integrity by injecting false data. The transmission of error data depletes the constrained battery power and degrades the bandwidth utilization. False data can be injected by compromised sensor nodes in various ways, including data aggregation and relay. In WSNs, data aggregation is performed by sensor nodes, called data aggregators. This project is to detect false data injected by a data aggregator while performing data aggregation. Data aggregation is implemented in WSNs to eliminate data redundancy, reduce the energy consumption and improve data accuracy. To support data aggregation along with false data detection, every node will be monitored along with Naïve Bayes Detector and Fuzzy logic model. By using two detectors we can find the fault data easily and increase the accuracy of the network.

Abstract—In smart grid, the local data aggregator of an area collects the total electrical consumption of the area by aggregating the measurements of smart meters (SMs). A great number of schemes based on the cryptography have been proposed to guarantee the privacy of the individual measurements of the SMs. However, the cryptographic systems require the compli- cated computations and key-management infrastructural support as well as introduce information leaking risks. Therefore, in this paper, we propose a novel physical channel-based scheme for privacy preservation in data aggregation, which can work without a cryptographic system. The SMs preserve the privacy of the measurements by adding the jamming signals, which are constructed by scaled channels.The jamming signals are designed to be cancelled with each other at the aggregator. Concurrently, our scheme can resist different types of attacks such as eavesdrop- ping, compromising, and differential attacks even in the case that they are colluding. The simulation results show that the mean squared error (MSE) of the total measurements is significantly lower than that of the traditional scheme while the privacy of the individual measurements is high.

The Wireless Mobile wireless sensor network can be simply definedas WSN with mobile as sensor nodes.These nodes consistof a radio transreceiver and a microcontroller powered by abattery.The topology used for these network is not decided.So,routing becomes challenging job.Data Aggregation is nothing but collection of data fromdifferent resouces or nodes and giving output as asummary.The aggregation statistics are normally computedperiodically to analyse its pattern.The source informationfor data aggregators may originate from public records anddatabases,the information is packaged into aggregate reportsand then may sold to different agencies.These reports can beused in background checks and to make some decisions.Mostof the works in this consider that the aggregator is trusted.Butthis is not the case each time.The challenge is to protectdata when the aggregator is untrusted.Many of the recentworks[2][3],consider the time series data and untrustedaggregator.In this, for the purpose of protection of data ,a new encryption scheme is introduced. In this schemes ,aggregatordecrypts only the sum of all users data instead of individualusers data.

Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven an efficient utilization of power is essential in order to use networks for long duration hence it is needed to reduce data traffic inside sensor networks, reduce amount of data that need to send to base station. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Wireless sensor networks (WSN) offers that sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which is an attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have limited computational power and limited memory and battery power, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. In this paper, a data aggregation framework on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network.

[1] Yuan Zhang, Qingjun Chen, and Sheng Zhong Privacy-Preserving Data Aggregation in Mobile Phone Sensing IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 5, MAY 2016 [2] Abdel-shakour Abuzneid, Tarek Sobh, and Miad Faezipour. An enhanced communication protocol for anonymity and location privacy in wsn. In Proceedings of the IEEE Wireless Communications and Networking Conference, New Orleans, LA, USA, country, volume 912, 2015.

Distributed Compressed sensing (DCS) [3] is another solution for data aggregation, where random measurements are transmitted from each sensor and the data are recovered at the joint decoder. As the measurements are sent directly from the sensor to the sink node, this architecture leads to significant and unbalanced battery consumption in large-scale setups. hop transmission with the main goal to balance the power consumption of the sensing devices. Using compressed sensing we can reduce the sampling and ts for sensing signals that have a compressible representation (i.e.) the number of measurements that need to be stored are vastly reduced. This makes signal processing and reconstruction much simpler and has a wide variety of application in the real world including Photography, Holography, digital image processing etc. Such that they have scale data aggregation mechanism that is based on an extension of the framework in [4], [5] which addressed the problem of compressed sensing with side information. -hop data transmission scenario, which lies in contrast with the transmission mechanism. Data Gathering with Compressed Sensing

In the present distributed environment often we need to collect data from different sources adjust it, aggregate it and display it on screen or store it for future processing. Source format can be structured (hierarchical or tabular) or semi-structured (free text). There are few architectural patterns for building systems to collect and process data but mainly we can divide them into two groups, namely centralized and distributed. Centralized approach means that we have a single point of control over what and when we process, whether distributed applications are built as a bunch of independent nodes where each one performs a single task, independently, and communicates with the rest of the nodes via a messaging system. Maintaining changes in centralized ap- plication reflect in redeploy of the whole application. Implementing pluggable archi- tecture also is not an easy solution. On the other hand, distributed approach has it benefits since we can maintain each node (worker) independently to keep it up to date with the changes in the source.

In which a set of sensors is consigned as data aggregators in the settled regions of the sensor framework. The sensors in a skeleton send the data package directly to the aggregator of that network. Hence, the sensors in a network don't relate with each other. In-framework, gathering is similar to grid based data amassing with two noteworthy differentiations; each sensor inside a system talks with its neighboring center point. Any center inside a system can acknowledge the piece of aggregator center in regards to adjusts until the last center kicks the container. This is similar to gathering based data combination in which the group heads are settled. In-framework accumulation, the sensor with the most separating information adds up to the data packages and sends the entwined data to the sink. Each sensor transmits its banner quality to its neighbors.