Aggregation In Sensor Networks Research Articles

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

An innovative framework for balanced cluster‐based data aggregation in sensor networks

SummaryThe essential design concern of a sensor network is to balance energy consumption of sensor nodes (SNs) to prolong the network lifetime. Many research works cited that clustering techniques efficiently utilize the network's energy resource by organizing SNS into groups of clusters and benefits in reduced data transmission. An extensively used category of cluster‐based protocols is the probabilistic clustering technique in which a preset optimum likelihood is used to facilitate the selection procedure of cluster head (CH). These clustering techniques suffer from non‐uniform dissemination of CH, which leads to uneven load balance and uneven energy consumption of SNs during network activities like data transmitting and data receiving. This causes an energy‐hole problem and reduces network lifetime. In order to solve these issues, we have focused on to design a balanced cluster‐based data aggregation and formulate a method that increases the energy efficiency of probabilistic clustering techniques by optimizing the number of clusters and the dissemination of CHs in the sensor network. The simulation analysis proves that the proposed technique accomplishes significantly enhanced than the existing works.

An innovative framework for balanced cluster-based data aggregation in Sensor Networks

The essential design concern of a sensor network is to balance energy consumption of sensor nodes (SNs) to prolong the network lifetime. Many research works cited that clustering techniques efficiently utilize the network's energy resource by organizing SNS into groups of clusters and benefits in reduced data transmission. An extensively used category of cluster-based protocols is the probabilistic clustering technique in which a preset optimum likelihood is used to facilitate the selection procedure of cluster head (CH). These clustering techniques suffer from non-uniform dissemination of CH, which leads to uneven load balance and uneven energy consumption of SNs during network activities like data transmitting and data receiving. This causes an energy-hole problem and reduces network lifetime. In order to solve these issues, we have focused on to design a balanced cluster-based data aggregation and formulate a method that increases the energy efficiency of probabilistic clustering techniques by optimizing the number of clusters and the dissemination of CHs in the sensor network.

A geodesic deployment and radial shaped clustering (RSC) algorithm with statistical aggregation in sensor networks

Wireless sensor networks (WSN) comprise a large number of connected tiny or small sensor devices to sense physical phenomenon. In WSN, prolonging the network's lifetime is a biggest challenge due to absence of power harvesting facility and irreplaceable batteries of the sensor devices. Clustering is one of the widely accepted and standard technique to solve the energy issues faced in WSN. In addition to clustering, the shape of the deployment area also plays the major role especially for large scale sensor deployment. This paper proposes a radial shaped clustering (RSC) algorithm with angular inclination routing. The radial shaped deployed area is divided into virtual concentric layers and each layer is further divided into a set of sectors called clusters. Angular routing is applied to achieve multihop routing of packets towards the Sink node. In comparison to fan shaped clustering (FSC), RSC performs better in terms of residual energy and packet received ratio.

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks.

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

Maximising energy efficiency using data aggregation in sensor networks

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.

Maximising energy efficiency using data aggregation in sensor networks

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.

Design and Simulation of a Blacklisting Technique for Detection of Hello flood Attack on LEACH Protocol

Wireless sensor networks consist of number of small, low power nodes with limited computational capabilities. In such networks, data is collected from various low power nodes by nodes with higher energy than others, called cluster heads. These cluster heads then send this data to a major node called sink. However, this aggregation is prone to many attacks. This paper intends to present a study of the algorithms, protocols and techniques for secure data aggregation in sensor networks. The Hello flood attack is explained and a novel algorithm for defense against Hello flood attack is proposed and simulated using Matlab.

Linear approaches to resilient aggregation in sensor networks

Abstract A sensor network is a network comprised of many small, wireless, resource-limited nodes that sense data about their environment and report readings to a base station. One technique to conserve power in a sensor network is to aggregate sensor readings hop-by-hop as they travel towards a base station, thereby reducing the total number of messages required to collect each sensor reading. In an adversarial setting, the ability of a malicious node to alter this aggregate total must be limited. We present three aggregation protocols inspired by three natural key pre-distribution schemes for linear networks. Assuming no more than k consecutive nodes are malicious, each of these protocols limits the capability of a malicious node to altering the aggregate total by at most a single valid sensor reading. Additionally, our protocols are able to detect malicious behavior as it occurs, allowing the protocol to be aborted early, thereby conserving energy in the remaining nodes. A rigorous proof of security is given for each protocol. We then demonstrate how to extend our linear protocols to tree-based topologies, thereby allowing linear-based approaches to be applied in a much wider range of network topologies.

An Efficient and Verifiable Privacy-Preserving Data Aggregation Scheme for Wireless Sensor Networks

Data aggregation is an important techniques to save energy consumption in wireless sensor networks. However, the compromised aggregators may forge fake values as the aggregated results of their child nodes. In this paper, we proposed an efficient secure data aggregation scheme to overcome the problem. Integrated with the privacy homomorphism and the aggregate message authentication code techniques, our proposal can provide both end-to-end privacy and data integrity in data aggregation for sensor networks. The performance analysis shows that compared with other existing secure data aggregation schemes, the proposed scheme has remarkable advantages in terms of computation and communication overhead.

Improved Cluster Head Selection for Efficient Data Aggregation in Sensor Networks

Large-scale Wireless Sensor Networks (WSN) is the focus of recent research and development efforts. Due to their benefits in monitoring physical environments, WSN find diverse applications from military usage to agriculture and scientific works. To maximize WSN’s network life, data transfer paths are selected so that total energy consumed on the path is minimal. To ensure high scalability and improved data aggregation, sensor nodes are grouped into disjoint, non-overlapping subsets known as clusters. This study proposes improved Cluster Head (CH) selection for efficient sensor networks’ data aggregation. The suggested hybrid algorithm is based on Bacterial Foraging Optimization (BFO) and Gravitational Search Algorithm (GSA). The proposed hybrid BFO is incorporated in Lower Energy Adaptive Clustering Hierarchy (LEACH).

Two security layers for hierarchical data aggregation in sensor networks

Due to resource restricted sensor nodes, it is important to minimise the amount of data transmission among sensor networks. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, as sensor networks are often deployed in untrusted and even hostile environments, sensor nodes are prone to node compromise attacks. Hence, an end-to-end secure aggregation approach is required to ensure a healthy data reception. In this paper, we propose two layers for secure data aggregation in sensor networks. Firstly, we provide an end-to-end encryption scheme that supports operations over cypher-text. It is based on elliptic curve cryptography that exploits a smaller key size, allows the use of higher number of operations on cypher-texts and prevents the distinction between two identical texts from their cryptograms. Secondly, we propose a new watermarking-based authentication that enables sensor nodes to ensure the identity of other nodes they are communicating with. Our experiments show that our hybrid approach of secure data aggregation enhances the security, significantly reduces computation and communication overhead, and can be practically implemented in on-the-shelf sensor platforms.

An Efficient Data Aggregation Protocol Concentrated on Data Integrity in Wireless Sensor Networks

Wireless sensor networks consist of a great number of sensor nodes with strictly limited computation capability, storage, communication resources, and battery power. Because they are deployed in remote and hostile environments and hence are vulnerable to physical attacks, sensor networks face many practical challenges. Data confidentiality, data integrity, source authentication, and availability are all major security concerns. In this paper, we focus on the very problem of preserving data integrity and propose an Efficient Integrity-Preserving Data Aggregation Protocol (EIPDAP) to guarantee the integrity of aggregation result through aggregation in sensor networks. In EIPDAP, base station can immediately verify the integrity of aggregation result after receiving the aggregation result and corresponding authentication information. However, to check integrity, most existing protocols need an additional phase which will consume a lot of energy and cause network delay. Compared with other related schemes, EIPDAP reduces the communication overhead per node to [Formula: see text], where [Formula: see text] is the degree of the aggregation tree for the network. To the best of our knowledge, EIPDAP has the most optimal upper bound on solving the integrity-preserving data aggregation problem.

Optimal Function Computation in Directed and Undirected Graphs

We consider the problem of information aggregation in sensor networks, where one is interested in computing a function of the sensor measurements. We allow for block processing and study in-network function computation in directed graphs and undirected graphs. We study how the structure of the function affects the encoding strategies and the effect of interactive information exchange. Depending on the application, there could be a designated collector node, or every node might want to compute the function. We begin by considering a directed graph C = (γ. ε) on the sensor nodes, where the goal is to determine the optimal encoders on each edge which achieve function computation at the collector node. Our goal is to characterize the rate region in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|ε|</sup> , i.e., the set of points for which there exist feasible encoders with given rates which achieve zero-error computation for asymptotically large block length. We determine the solution for directed trees, specifying the optimal encoder and decoder for each edge. For general directed acyclic graphs, we provide an outer bound on the rate region by finding the disambiguation requirements for each cut, and describe examples where this outer bound is tight. Next, we address the scenario where nodes are connected in an undirected tree network, and every node wishes to compute a given symmetric Boolean function of the sensor data. Undirected edges permit interactive computation, and we therefore study the effect of interaction on the aggregation and communication strategies. We focus on sum-threshold functions and determine the minimum worst case total number of bits to be exchanged on each edge. The optimal strategy involves recursive in-network aggregation which is reminiscent of message passing. In the case of general graphs, we present a cut-set lower bound and an achievable scheme based on aggregation along trees. For complete graphs, we prove that the complexity of this scheme is no more than twice that of the optimal scheme.

A Lifetime-Preserving and Delay-Constrained Data Gathering Tree for Unreliable Sensor Networks

A tree routing structure is often adopted for many-to-one data gathering and aggregation in sensor networks. For real-time scenarios, considering lossy wireless links, it is an important issue how to construct a maximum-lifetime data gathering tree with delay constraint. In this work, we study the problem of lifetime-preserving and delay-constrained tree construction in unreliable sensor networks. We prove that the problem is NP-complete. A greedy approximation algorithm is proposed. We use expected transmissions count (ETX) as the link quality indicator, as well as a measure of delay. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes by pruning and grafting its sub-tree. The complexity of the proposed algorithm is O(N4). Finally, extensive simulations are carried out to verify our approach. Simulation results show that our algorithm provides longer lifetime in various situations compared to existing data gathering schemes.

Achieving Max–Min lifetime and fairness with rate allocation for data aggregation in sensor networks

We consider the rate allocation problem for data aggregation in wireless sensor networks with two objectives: (1) maximizing the minimum (Max–Min) lifetime of an aggregation cluster and (2) achieving fairness among all data sources. The two objectives are generally correlated with each other and usually, they cannot be maximized simultaneously. We adopt a lexicographic method to solve this multi-objective programming problem. First, we recursively induce the Max–Min lifetime for the aggregation cluster. Under the given Max–Min lifetime, we then formulate the problem of maximizing fairness as a convex optimization problem, and derive the optimal rate allocation strategy by iterations. We also present low-complexity algorithms that an aggregation cluster can use to determine the Max–Min network lifetime and the fair rate allocation. Our simulation results validate our analytical results and illustrate the effectiveness of the approach.

Everywhere sparse approximately optimal minimum energy data gathering and aggregation in sensor networks

We consider two related data gathering problems for wireless sensor networks (WSNs). The MLDA problem is concerned with maximizing the system lifetime T so that we can perform T rounds of data gathering with in-network aggregation, given the initial available energy of the sensors. The M 2 EDA problem is concerned with minimizing the maximum energy consumed by any one sensor when performing T rounds of data gathering with in-network aggregation, for a given T . We provide an effective algorithm for finding an everywhere sparse integral solution to the M 2 EDA problem which is within a factor of α = 1+ 4 n / T of the optimum, where n is the number of nodes. A solution is everywhere sparse if the number of communication links for any subset X of nodes is O ( X ), in our case at most 4| X |. Since often T = ω( n ), we obtain the first everywhere sparse, asymptotically optimal integral solutions to the M 2 EDA problem. Everywhere sparse solutions are desirable since then almost all sensors have small number of incident communication links and small overhead for maintaining state. We also show that the MLDA and M 2 EDA problems are essentially equivalent, in the sense that we can obtain an optimal fractional solution to an instance of the MLDA problem by scaling an optimal fractional solution to a suitable instance of the M 2 EDA problem. As a result, our algorithm is effective at finding everywhere sparse, asymptotically optimal, integral solutions to the MLDA problem, when the initial available energy of the sensors is sufficient for supporting optimal system lifetime which is ω( n ).

Time-decaying Sketches for Robust Aggregation of Sensor Data

We present a new sketch for summarizing network data. The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks: the sketch is duplicate insensitive, i.e., reinsertions of the same data will not affect the sketch and hence the estimates of aggregates. Unlike previous duplicate-insensitive sketches for sensor data aggregation [S. Nath et al., Synposis diffusion for robust aggregation in sensor networks, in Proceedings of the 2nd International Conference on Embedded Network Sensor Systems, (2004), pp. 250–262], [J. Considine et al., Approximate aggregation techniques for sensor databases, in Proceedings of the 20th International Conference on Data Engineering (ICDE), 2004, pp. 449–460], it is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function. The sketch can give provably approximate guarantees for various aggregates of data, including the sum, median, quantiles, and frequent elements. The size of the sketch and the time taken to update it are both polylogarithmic in the size of the relevant data. Further, multiple sketches computed over distributed data can be combined without loss of accuracy. To our knowledge, this is the first sketch that combines all the above properties.

Latency-constrained aggregation in sensor networks

A sensor network consists of sensing devices which may exchange data through wireless communication; sensor networks are highly energy constrained since they are usually battery operated. Data aggregation is a possible way to save energy consumption: nodes may delay data in order to aggregate them into a single packet before forwarding them towards some central node (sink). However, many applications impose constraints on the maximum delay of data; this translates into latency constraints for data arriving at the sink. We study the problem of data aggregation to minimize maximum energy consumption under latency constraints on sensed data delivery, and we assume unique communication paths that form an intree rooted at the sink. We prove that the offline problem is strongly NP-hard and we design a 2-approximation algorithm. The latter uses a novel rounding technique. Almost all real-life sensor networks are managed online by simple distributed algorithms in the nodes. In this context we consider both the case in which sensor nodes are synchronized or not. We assess the performance of the algorithm by competitive analysis. We also provide lower bounds for the models we consider, in some cases showing optimality of the algorithms we propose. Most of our results also hold when minimizing the total energy consumption of all nodes.

Security model and framework for information aggregation in sensor networks

Information aggregation is an important operation in wireless sensor networks (WSNs) executed for the purpose of monitoring and reporting environmental data. Due to the performance constraints of sensor nodes the in-network form of the aggregation is especially attractive since it allows saving expensive resources during frequent network queries. Easy accessibility of networks and nodes and almost no physical protection against corruptions raise high security challenges. Protection against attacks aiming to falsify the aggregated result is considered to be of prime importance. In this article we design the first general framework for secure information aggregation in WSNs focusing on scenarios where aggregation is performed by one of its nodes. The framework achieves security against node corruptions and is based solely on the symmetric cryptographic primitives that are more suitable for WSNs in terms of efficiency. We analyze performance of the framework and unlike many previous approaches increase confidence in it by a rigorous proof of security within the specially designed formal security model.