Data Aggregation Protocol Research Articles

Two level data aggregation protocol for prolonging lifetime of periodic sensor networks

One big contributor in the future of the Internet of Things is the Periodic Sensor Networks (PSNs) because it has been used by many applications in real life. The main challenge in this type of networks is to gather the huge amount of data periodically in an energy saving way and then transmit them to the base station in order to extend the lifetime of PSN. Since the limited nature of the sensors batteries power, therefore, an energy-efficient data aggregation method is needed to optimize both energy and lifetime in PSNs. This article proposes a Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks. TLDA works in a periodic way. Each period consists of two data aggregation levels. The first level of data aggregation is applied at the sensor node. This level includes data collection, the sliding window to generate a varying number of segments with different lengths, and data aggregation using Adaptive Piecewise Constant Approximation technique to reduce the amount of data collected by each sensor. The second level is applied at the aggregator. It includes grouping received data sets based on the chaining hash table with SAX quantization method, finding and lowering the duplicate sets, finding and merging the duplicate readings, and transmit the aggregated data to the sink. Extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.

A UAV-assisted topology-aware data aggregation protocol in WSN

This paper studies an unmanned aerial vehicles (UAV) based data aggregation of wireless sensor networks (WSNs). Data aggregation approaches based on Compressive Sensing (CS) enjoy the benefits of having their traffic volume reduced. However, existing CS-based data aggregation schemes have drawbacks, such as errors in data reconstruction process and the large overheads in regenerating and broadcasting feasible measurement matrices when the network is scaled up. To this end, we propose a topology-aware data aggregation (TADA) protocol, which preserves the advantages of CS-based schemes while alleviating the aforementioned issues. Extensive performance comparisons demonstrate that the data reconstruction error rate of TADA is much lower than other CS-based schemes under test. More important, for most WSN applications, since the underlying physical layer prefers frames with small payloads, the proposed TADA utilizes less transmissions from sensors to UAV in each round of data aggregation, and thus can yields a lower energy consumption.

A study on data aggregation techniques in wireless sensor network in static and dynamic scenarios

Small-size sensor nodes are used as the basic component for collecting and sending the data or information in the ad hoc mode in wireless sensor network (WSN). This network is generally used to collect and process data from different regions where the movement of human is very rare. The sensor nodes are deployed in such a region for collecting data using ad hoc network where, at any time, the unusual situation may happen or there is no fixed network that can work positively and provide any transmission procedure. The location may be very remote or some disaster-prone area. In disaster-prone zone, after disaster, most often no fixed network remains alive. In that scenario, the ad hoc sensor network is one of the reliable sources for collecting and transmitting the data from that region. In this type of situation, sensor network can also be helpful for geo-informatic system. WSN can be used to handle the disaster management manually as well as through an automated system. The main problem for any activity using sensor node is that the nodes are very much battery hunger. An efficient power utilization is required for enhancing the network lifetime by reducing data traffic in the WSN. For this reason, some efficient intelligent software and hardware techniques are required to make the most efficient use of limited resources in terms of energy, computation and storage. One of the most suitable approaches is data aggregation protocol which can reduce the communication cost by extending the lifetime of sensor networks. The techniques can be implemented in different efficient manners, but all are not useful in same application scenarios. More specifically, data can be collected by dynamic approach using rendezvous point (RP), and for that purpose, intelligent neural network-based cluster formation techniques can be used and for fixing the targeted base station, the ant colony optimization algorithm can be used. In this work, we have made a comprehensive study of such energy efficient integrated sensor-based system in order to achieve energy efficiency and to prolong network lifetime.

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.

V-Matrix-Based Scalable Data Aggregation Scheme in WSN

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA) , which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA , as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA , which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.

Network lifetime improvement using landmark-assisted mobile sink scheduling for cyber-physical system applications

Improving the lifetime of a wireless sensor network is a challenging issue in the development of energy-efficient cyber-physical systems, especially in the design of sensor data aggregation protocols. Mobile sinks are typically used in this context to increase the lifetime of the sensor network in a cyber-physical system. In this work, a landmark-assisted mobile sink scheduling scheme is proposed for designing an energy-efficient data aggregation protocol. A landmark-node identification method along with network clustering is first developed using random walks over network graphs. Subsequently, using the locations of landmark-nodes, a mobile sink scheduling method is developed for sensor data aggregation in an extended Kalman filtering framework. This method is robust since location information of the landmark-nodes is also continuously updated in the Kalman filtering framework. Experiments on identifying landmark-nodes and mobile sink scheduling are conducted on the Intel Berkeley Research Lab dataset and over a real WSN test bed with a Kobuki–Yujin robot. Experimental results obtained for network lifetime analysis and energy-efficiency for various network configurations indicate an improvement over standard methods and are motivating enough for use in practical cyber-physical system applications.

A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

This study proposes a two-tier distributed fuzzy logic based protocol (TTDFP) to improve the efficiency of data aggregation operations in multihop wireless sensor networks (WSNs). Clustering is utilized for efficient aggregation requirements in terms of consumed energy. In a clustered network, member (leaf) nodes transmit obtained data to cluster-heads (CHs) and CHs relay received packets to the base station. In multihop wireless networks, this CH-generated transmission occurs over other CHs. Due to the adoption of a multihop topology, hotspots and/or energy-hole problems may arise. This article proposes a TTDFP to extend the lifespan of multihop WSNs by taking the efficiency of clustering and routing phases jointly into account. TTDFP is a distribution-adaptive protocol that runs and scales sensor network applications efficiently. Additionally, along with the two-tier fuzzy logic based protocol, we utilize an optimization framework to tune the parameters used in the fuzzy clustering tier in order to optimize the performance of a given WSN. This paper also includes performance comparisons and experimental evaluations with the selected state-of-the-art algorithms. The experimental results reveal that TTDFP performs better than any other protocols under the same network setup considering metrics used for comparing energy-efficiency and network lifespan of the protocols.

SPEEDA: A Secure Protocol and Energy Efficient for Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSN) consists of a set of small and resources-constrained devices. They have received much attention over the last few years for the study and development of a plethora of potential applications. However, the common denominator of all applications of these sensor networks is the vulnerability of micro-sensors because of their limited material resources the most constraining of which is energy. In fact, wireless sensors are limited in terms of calculation, storage, battery, etc. Therefore, every possible solution that aims to conserve these resources is extensively sought. Thus, a great deal of researches has been conducted leading to an effective technique answering the established problem. This solution concerns the aggregation of data which is one of the techniques that is actually considered as an essential paradigm for sensor networks since it tends to save computation and communication resources. Data aggregation allows in-network processing which leads to lesser packet transmissions and reduces redundancy, and therefore, helps in increasing network’s overall lifetime. However, sensor networks are usually deployed in unattended and hostile environments.Thus, the designer should not only consider the limited resources of the sensor nodes but also the security threats that can occur in an easily accessible network to the attacker. In this paper, we will give an overview study of the existing data aggregation solutions in wireless sensor networks then, we will propose a new efficient and secure approach to aggregation which can be evaluated with specific criteria.

Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.

Towards secure and truthful task assignment in spatial crowdsourcing

The ubiquity of mobile device and wireless networks flourishes the market of spatial crowdsourcing, in which location constrained tasks are sent to workers and expected to be performed in some designated locations. To obtain a global optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, the platform may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, to tackle the privacy-preserving task assignment problem, we propose a privacy-preserving reverse auction based assignment model which consists of two key parts. In the first part, we generalize private location to travel cost and protect it by an anonymity based data aggregation protocol. In the second part, we propose a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our model is efficient and can scale to real SC applications.

Congestion Minimizing Scheme for Enhanced Data Aggregation in ZigBee-Based SG AMI Network

It is envisaged that the multi-directional communication of end user’s energy consumption data in a smart grid advanced metering infrastructure (AMI) network needed for billing and other grid controlled purposes will raise concerns about the potential violations of the privacy of end users’ personal data. Preserving the privacy of consumers can be achieved by designing robust and resilient AMI communication architectures with state-of-the-art cryptographic algorithms and data aggregation protocols. However, research has shown that these aggregation protocols can result in a congestive network scenario in the absence of a proper congestion management scheme. Unfortunately, there is no efficient mechanism for congestion management in the current ZigBee standard. In this paper, a well-designed Ring Triangulation Communication Architecture (RTCA) is utilized in our congestion minimizing scheme for enhancing data aggregation in a ZigBee based SG AMI network. The RTCA is implemented with a well-constructed ZigBee data and its aggregation algorithms. The average throughput and latency results from our scheme indicates a significant reduction in transmission congestion as well as traffic overheads incurred during message aggregation relative to the congestion control schemes (ZCCF and ZTCC) proposed in the literature.

On practical privacy-preserving fault-tolerant data aggregation

In this paper, we propose a fault-tolerant privacy-preserving data aggregation protocol which utilizes limited local communication between nodes. As a starting point, we analyze the Binary Protocol presented by Chan et al. Comparing to previous work, their scheme guaranteed provable privacy of individuals and could work even if some number of users refused to participate. In our paper we demonstrate that despite its merits, their method provides unacceptably low accuracy of aggregated data for a wide range of assumed parameters and cannot be used in majority of real-life systems. To show this we use both analytic and experimental methods. On the positive side, we present a precise data aggregation protocol that provides provable level of privacy even when facing massive failures of nodes. Moreover, our protocol requires significantly less computation (limited exploiting of heavy cryptography) than most of currently known fault-tolerant aggregation protocols and offers better security guarantees that make it suitable for systems of limited resources (including sensor networks). Most importantly, our protocol significantly decreases the error (compared to Binary Protocol). However, to obtain our result we relax the model and allow some limited communication between the nodes. Our approach is a general way to enhance privacy of nodes in networks that allow such limited communication, i.e., social networks, VANETs or other IoT appliances. Additionally, we conduct experiments on real data (Facebook social network) to compare our protocol with protocol presented by Chan et al.

A Review of Data Aggregation Protocols in VANET and providing proposed protocol

A Review of Data Aggregation Protocols in VANET and providing proposed protocol

EZ-AG: structure-free data aggregation in MANETs using push-assisted self-repelling random walks

This paper describes EZ-AG, a structure-free protocol for duplicate insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a token that performs a self-repelling random walk in the network and aggregates information from nodes when they are visited for the first time. A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. In this paper, we show that a single step push phase at each node can significantly speed up the aggregation and eliminate this slow down. By doing so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3 simulations in networks ranging from 100 to 4000 nodes under different mobility models and node speeds. We also describe a hierarchical extension for EZ-AG that can produce multi-resolution aggregates at each node using only O(NlogN) messages, which is a poly-logarithmic factor improvement over existing techniques.

Research and Improvement of Wireless Sensor Network Secure Data Aggregation Protocol Based on SMART

The privacy-preserving of information is one of the most important problems to be solved in wireless sensor network (WSN). Privacy-preserving data aggregation is an effective way to protect security of data in WSNs. In order to deal with the problem of energy consumption of the SMART algorithm, we present a new dynamic slicing D-SMART algorithm which based on the importance degree of data. The proposed algorithm can decrease the communication overhead and energy consumption effectively while provide good performance in preserving privacy by the reasonable slicing based on the importance degree of the collected raw data. Simulation results show that the proposed D-SMART algorithm improve the aggregation accuracy, enhance the privacy-preserving, reduce the communication overhead to some extent, decrease the energy consumption of sensor node and prolong the network lifetime indirectly.

Fast computation method for privacy-preserving data aggregation protocol

Fast computation method for privacy-preserving data aggregation protocol

Fast computation method for privacy-preserving data aggregation protocol

Recent years have seen the rapid improvement of smart terminals and wireless networks, and a lot of smart applications have come to the fore. Taking advantages of fast developed smart terminals, more and more complicated sensing tasks can be taken over and finished. Data aggregation is an important application which is required by many tasks. The sensing devices in mobile sensing systems get time-series data, which can be used for supply forecast and leakage detection. As people nowadays pay more attention on privacy issues, to let people use today's data aggregation application, the first task is to solve their privacy concerns. Here, we propose a fast encryption/decryption mechanism for addition operations on keyed hash functions, based on multi-threads computation and parallel computation on multi-cores architecture. The advanced protocol presented by us works better for large systems and requires less time cost for both mobile sensors and aggregator. The experiments show the performance benefits of the presented protocol.

Unfailing secure data aggregation protocol in wireless sensor networks

Unfailing secure data aggregation protocol in wireless sensor networks

An Identity-Based Data Aggregation Protocol for the Smart Grid

The smart grid significantly improves the reliability, efficiency, security, and sustainability of electricity services. It plays an important role in modern energy infrastructure. A drawback of this new technique, however, is that the fine-grained metering data may leak private customer information. Thus, various public-key based data aggregation protocols for privacy protection have been proposed. However, the National Institute of Standards and Technology has recommended not using public-key based cryptography in the smart grid, since maintaining the public-key infrastructure is a heavy cost. In this paper, we propose an identity-based data aggregation protocol for the smart grid, which cannot only prevent unauthorized reading and fine-grained analyzing but can also protect against unintentional errors and maliciously altered messages. The basic building block of our protocol is an identity-based encryption and signature scheme in which an identity-based encryption scheme is combined with an identity-based signature scheme. They share the same private/public parameters, which greatly reduces the complexity of the protocol in the smart grid. Security analysis demonstrates the effectiveness of our protocol in the context of six typical attacks against the smart grid. A prototype implementation based on the Intel Edison platform shows that our protocol is efficient enough for physically constrained smart grid operators, such as smart meters.

Light-weight and secure aggregation protocols based on Bloom filters✰

In this paper we present a light-weight data aggregation protocol based on probabilistic data structure used for representing a subset of elements called Bloom filter. Our construction is optimal (up to a small multiplicative constant) in terms of the size of the aggregated data representation. Moreover, all necessary operations have minimal computational and memory requirements (no asymmetric cryptography is used) and provides data confidentiality in a model with an adversary with moderate capabilities. This protocol is adequate for systems of very constrained devices (like sensors) and is based solely on Bloom filters. Finally, we present two modifications of the basic protocol that can provide immunity against substantially stronger models of adversaries for the price of slightly more complex computations and increased size of the transmitted data.The security results of our protocol are mainly based on analysis of some properties of Bloom filters. We first investigate how much information about the number of represented elements can be learned by an external observer given a Bloom filter (or a part of it). The information leakage is expressed in terms of (α, β)-indistinguishability notion. We believe that this algorithm is quite general and can be applied for security analysis of other communication protocols based on Bloom filters.