A multi-Mobile Agent and optimal itinerary planning-based data aggregation in Wireless Sensor Networks

This paper deals with information discovery and aggregation in large scale wireless sensor networks applied for mission-critical applications like military reconnaissance. To support query processing based on the gathered information, an efficient and reliable information discovery mechanism is proposed for sensor networks. We extend the basic Comb-Needle Discovery Support Model [3] by including Cluster-based data aggregation mechanism, which helps minimize the communication cost. Clusterbased approach groups the sensor nodes in the sensor network. Each node of a group will send information to its Cluster Head, which then aggregates and forwards the information to the base station (Sink). We compare the performance of proposed Cluster-based Comb-Needle Model with the basic Comb-Needle Model using the parameters, namely, energy consumption and communication cost. The experimental results through Avrora Simulator on TinyOS Platform reveal that Clusterbased Comb-Needle Model for Data Aggregation helps a sensor network in conserving its energy, and thereby extending its life time.

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.

<p>Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.</p>

This paper attempts to identify the reliability assessment in terms of data aggregation in wireless sensor networks. With the sensor nodes' Bayesian lifetime model, the fault ratio of sensing data among a pair of nodes in the worst case is derived. Leveraging the property of exponential aggregation latency distribution, the optimizing strategy, namely fault-tolerant scheduling for aggregation (FTSA) is proposed. Quantitative analysis of the task scheduling is also presented, which is used for reducing the latency of data aggregation. Moreover, the sensor fault probability and aggregation latency are initially introduced into the data collection scheduling process, and it mathematically shows that the fused local estimate can obtain a global estimation with the reliable data acquisition framework. Performance evaluations of the proposed approach with both sensor node faults and scheduling policy are carried out. These results show that the proposed approach outperforms the earlier algorithms in terms of latency designed without regard to sensor faults.

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.

Data aggregation in wireless sensor networks using ant colony algorithm

In‐network data aggregationin wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead—sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure.The contributions of this paper are two‐fold: first, we design a private data aggregation protocol that does not leak individual sensed values during the data aggregation process. In particular, neither the base station (BS) nor the other nodes are able to compromise the privacy of an individual node's sensed value. Second, the proposed protocol is robust to data‐loss; if there is a node‐failure or communication failure, the protocol is still able to compute the aggregate and to report to the base station the number of nodes that participated in the aggregation. To the best of our knowledge, our scheme is the first one that efficiently addresses the above issues all at once. Copyright © 2009 John Wiley & Sons, Ltd.

In-network data aggregation plays an important role to reduce the transmitting energy and data redundancy in large scale sensor networks. However, the security of wireless sensor networks is a challenging problem in the process of data aggregation. An efficient secure data aggregation is proposed to enhance the data security of wireless sensor networks. Firstly, the secure in-network aggregation tree is introduced, then a judgment method based on trust schema was used to detect whether a sensor node has potential misbehavior. After the detection, a local recovery schema is proposed to reduce the possibility to become isolated nodes, which will increase the security level in data aggregation in wireless sensor networks. The performance analysis shows the efficiency of the schema.

Data aggregation has been one of the most widely researched topics for Wireless Sensor Network (WSN) as it helps in increasing the effective life time of the network and reduces the packet transmission overhead. Besides other challenges, security is one of the serious challenges during data aggregation. Numerous solutions on security for data aggregation for WSN are based on homomorphic encryption technique or Elliptic Curve Cryptography (ECC) based solutions and thus are vulnerable to attacks by quantum machines. This paper presents a post-quantum security solution using lattice cryptography. The proposed solution is applying Learning with Errors over Rings (R-LWE) for encryption of data in the data aggregation process at the gateway module of WSN. Security analysis shows that the proposed scheme provides confidentiality, integrity and authenticity during communication. Performance analysis shows that the proposed scheme is lightweight and shows better performance compared to Elliptic Curve Elgamal (ECEG) cryptography based scheme and symmetric homomorphic based scheme.

Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. The major problem in the field of wireless network is draining of sensor's battery. As the senor nodes are driving high power so there is a need of an intelligent algorithm which could enable the sensors to utilize the energy in an energy efficient manner. It will also enable the network to work for longer duration of time as well as reduces trafficking inside network. Data Aggregation is a technique which can solve these problems. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. It involves the selective transfer of data from the nodes to the base station. Wireless sensor networks (WSN) offer an increasingly 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 attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. 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. Also the paper briefly discusses a case of sending the temperature reading from the source node to the destination. It also provides techniques in order to send the minimum data at the cost of a small error. The aim of the proposed work is to compare the performance of SPIN in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks and to assess the suitability of the protocol in an environment where resources are limited.

Integrity protecting hierarchical concealed data aggregation for wireless sensor networks

Sensor Networks have recently emerged as a ubiquitous computing platform. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. In this work, we propose a self-organizing method for aggregating data in ad-hoc wireless sensor networks. We present new network architecture, CODA (Cluster-based self-Organizing Data Aggregation), based on the Kohonen Self-Organizing Map to aggregate sensor data in cluster. Before deploying the network, we train the nodes to have the ability to classify the sensor data. Thus, it increases the quality of data and reduces data traffic as well as energy-conserving. Our simulation results show that CODA increases the accuracy of data than traditional aggregation of database system. Finally, we show a real-world platform, TIP, on that we will implement the idea.

Data gathering in wireless sensor network (WSN) has attracted a lot of attention in research. Data gathering can be done with or without aggregation, depending on the degree of correlation among the source data. In this paper, we study the problem of data gathering without aggregation, aiming to conserving the energy of sensor nodes so as to maximize the network lifetime. We model the problem as one of finding a min-max-weight spanning tree (MMWST) , which is shown to be NP-complete. In MMWST, the maximum weight of the nodes is minimized. The weight of a node in the tree equals the ratio of the number of the node's descendants to the node's energy. We propose a Ω(log n/ log log n)-approximation centralized algorithm MITT to construct MMWST without requiring node location information. Moreover, in order to enable MITT to be used in large-scale networks, a new solution that employs the clustering technique is proposed. To the best of our knowledge, MITT is the first algorithm that constructs MMWST in wireless sensor networks. Theoretical analyses and simulation results show that MITT can achieve longer network lifetime than existing data gathering algorithms.

In wireless sensor networks, sensor nodes have limited battery power. Data aggregation is an important technique in a wireless sensor network to reduce the energy consumption by eliminating the redundant data and delivering useful data to the user. Data aggregation technique is used to gather the data sensed by the sensor nodes in the network and aggregates the sensed data in an energy-efficient way so that overall network lifetime can be increased. Cluster, tree, chain, and grid are some of the architectures for data aggregation in wireless sensor networks. In this paper, authors present an analysis-based survey of data aggregation protocols for tree-based architecture in wireless sensor networks. The authors analyze each algorithm on the basis of performance measurements such as network lifetime, energy consumption, and node distance. Authors also compare these algorithms on the basis of the preferred parameter and a key feature of each algorithm. In the fifth section of the paper, authors described the proposed approach to construct the data aggregation tree to maximize the network lifetime. In the proposed approach, authors used distance parameter to construct minimum spanning tree, and other parameters such as load and energy are preferred in a balanced manner to reduce the energy consumption and to maximize the network lifetime.