Efficient clustering-based data aggregation techniques for wireless sensor networks

In wireless sensor network applications for surveillance and reconnaissance, large amounts of redundant sensing data are frequently generated. It is important to control these data with efficient data aggregation techniques to reduce energy consumption in the network. Several clustering methods were utilized in previous works to aggregate large amounts of data produced from sensors in target tracking applications (Park in A dissertation for Doctoral in North Carolina State University, 2006). However, such data aggregation algorithms show effectiveness only in restricted environments, while posing great problems when adapting to other various situations. To alleviate these problems, we propose two hybrid clustering based data aggregation mechanisms. The combined clustering-based data aggregation mechanism can apply multiple clustering techniques simultaneously in a single network depending on the network environment. The adaptive clustering-based data aggregation mechanism can adaptively choose a suitable clustering technique, depending on the status of the network. The proposed mechanisms can increase the data aggregation efficiency as well as improve energy efficiency and other important issues compared to previous works. Performance evaluation via mathematical analysis and simulation has been made to show the effectiveness of the proposed mechanisms.     

A time efficient aggregation convergecast scheduling algorithm for wireless sensor networks

Wireless Networks - We investigated the aggregation convergecast scheduling problem in wireless sensor networks. In order to reduce the time needed for data collection through aggregation...     

Adaptive data aggregation with probabilistic routing in wireless sensor networks

Periodical extraction of raw sensor readings is one of the most representative and comprehensive applications in Wireless sensor networks. In order to reduce the data redundancy and the communication load, in-network data aggregation is usually applied to merge the packets during the routing process. Aggregation protocols with deterministic routing pre-construct the stationary structure to perform data aggregation. However, the overhead of construction and maintenance always outweighs the benefits of data aggregation under dynamic scenarios. This paper proposes an Adaptive Data Aggregation protocol with Probabilistic Routing for the periodical data collection events. The main idea is to encourage the nodes to use an optimal routing structure for data aggregation with certain probability. The optimal routing structure is defined as a Multi-Objective Steiner Tree, which can be explored and exploited by the routing scheme based on the Ant Colony Optimization and Genetic Algorithm hybrid approach. The probabilistic routing decision ensures the adaptability for some topology transformations. Moreover, by using the prediction model based on the sliding window for future arriving packets, the adaptive timing policy can reduce the transmission delay and can enhance the aggregation probability. Therefore, the packet transmission converges from both spatial and temporal aspects for the data aggregation. Finally, the theoretical analysis and the simulation results validate the feasibility and the high efficiency of the novel protocol when compared with other existing approaches.     

Non-intrusive aggregation in wireless sensor networks

Since energy is scarce in sensor nodes, wireless sensor networks aim to transmit as few packets as possible. To achieve this goal, sensor protocols often aggregate measured data from multiple sensor nodes into a single packet. In this paper, a survey of aggregation techniques and methods is given. Based on this survey, it is concluded that there are currently several dependencies between the aggregation method and the behavior of the other network layers. As a result, existing aggregation methods can often not be combined with different routing protocols. To remedy this shortcoming, the paper introduces a new ‘non-intrusive’ aggregation approach which is independent of the routing protocol. The proposed aggregation method is evaluated and compared to traditional aggregation approaches using a large-scale sensor testbed of 200 TMoteSky sensor nodes. Our experimental results indicate that existing aggregation approaches are only suited for a limited set of network scenarios. In addition, it is shown both mathematically and experimentally that our approach outperforms existing non-intrusive techniques in a wide range of scenarios.     

Distributed wake-up scheduling for data collection in tree-based wireless sensor networks

In a multi-hop wireless network, a conventional way of defining interference neighbors is to prohibit a node from using the same slot/code as those of its 1-hop and 2-hop neighbors. However, for data collection in a wireless sensor network, since the set of communication nodes is limited and the transmission directions are toward the sink, we show that a less strict set of interference neighbors can be defined. Based on this observation, we develop an efficient distributed wake-up scheduling scheme for data collection in a sensor network that achieves both energy conservation and low reporting latency.     

Break-and-join tree construction for latency-aware data aggregation in wireless sensor networks

Wireless Networks - Emerging applications require processing a huge amount of environmental data from wireless sensor networks, and then triggering appropriate actions in response to the detected...     

A hierarchical energy-efficient framework for data aggregation in wireless sensor networks

A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, are often aggregated (or compressed). This compression is done by a subset of the sensors called "aggregators." Inasmuch as sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, the authors first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS) protocol. Then, they generalize it to an aggregation hierarchy and extend EPAS to Hierarchical EPAS. The optimal number of aggregators with generalized compression and power-consumption models was derived, and fully distributed algorithms for aggregator selection were presented. Simulation results show that the algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols and are thus applicable to a broad spectrum of application environments.     

Secure data aggregation without persistent cryptographic operations in wireless sensor networks

In-network data aggregation is an essential operation to reduce energy consumption in large-scale wireless sensor networks. With data aggregation, however, raw data items are invisible to the base station and thus the authenticity of the aggregated data is hard to guarantee. A compromised sensor node may forge an aggregation value and mislead the base station into trusting a false reading. Due to the stringent constraints of energy supply and computing capability on sensor nodes, it is challenging to detect a compromised sensor node and keep it from cheating, since expensive cryptographic operations are unsuitable for tiny sensor devices. This paper proposes a secure aggregation tree (SAT) to detect and prevent cheating. Our method is essentially different from other existing solutions in that it does not require any cryptographic operations when all sensor nodes work honestly. The detection of cheating is based on the topological constraints in the aggregation tree. We also propose a weighted voting scheme to determine a misbehaving node and a secure local recovery scheme to avoid using the misbehaving node.     

Aggregation convergecast scheduling in wireless sensor networks

We consider the problem of scheduling in wireless sensor networks for the purposes of aggregation convergecast. We observe that existing schemes adopt essentially a two phase approach, consisting of, first, a tree construction and, second, a scheduling phase. Following a similar approach, we propose two new improvements, one to each of the two phases. Starting with a new lower bound on the schedule length, we make use of it in the tree construction phase. The tree construction phase consists of solutions to instances of bipartite graph semi-matchings. The scheduling phase is a weight-based priority scheme that obeys dependency (tree) and interference constraints. Our extensive experiments show that, overall, our proposed solution not only outperforms all previously proposed solutions in terms of schedule length, but it also significantly extends the network’s lifetime.     

An adaptive-weighted two-dimensional data aggregation algorithm for clustered wireless sensor networks

In this paper, an Adaptive-Weighted Time-Dimensional and Space-Dimensional （AWTDSD） data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network. AWTDSD contains three phases： （1） the time-dimensional aggregation phase for eliminating the data redundancy; （2） the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and （3） the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station. AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well. Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.     

TDMA scheduling algorithms for wireless sensor networks

Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.     

Time synchronization algorithm of wireless sensor networks based on data aggregation tree

Time synchronization is a critical technique of wireless sensor networks (WSNs). Aimed at the disadvantages of hierarchical time synchronization algorithm, this paper proposes an improved algorithm used for WSNs based on data aggregation tree. Based on collection and selection principles of multiple time data point tuples, the relative time drift and phase offset of the node are calculated through linear programming method, and time synchronization is achieved eventually during the establishment of data aggregation tree. Performance analysis and simulation results prove that, compared with the existing time synchronization algorithms, the proposed algorithm can reduce the energy cost of nodes and shorten the time of synchronization effectively.     

Secure encrypted-data aggregation for wireless sensor networks

This paper proposes a secure encrypted-data aggregation scheme for wireless sensor networks. Our design for data aggregation eliminates redundant sensor readings without using encryption and maintains data secrecy and privacy during transmission. Conventional aggregation functions operate when readings are received in plaintext. If readings are encrypted, aggregation requires decryption creating extra overhead and key management issues. In contrast to conventional schemes, our proposed scheme provides security and privacy, and duplicate instances of original readings will be aggregated into a single packet. Our scheme is resilient to known-plaintext attacks, chosen-plaintext attacks, ciphertext-only attacks and man-in-the-middle attacks. Our experiments show that our proposed aggregation method significantly reduces communication overhead and can be practically implemented in on-the-shelf sensor platforms.     

Energy-neutral scheduling and forwarding in environmentally-powered wireless sensor networks

In environmentally-powered wireless sensor networks (EPWSNs), low latency wakeup scheduling and packet forwarding is challenging due to dynamic duty cycling, posing time-varying sleep latencies and necessitating the use of dynamic wakeup schedules. We show that the variance of the intervals between receiving wakeup slots affects the expected sleep latency: when the variance of the intervals is low (high), the expected latency is low (high). We therefore propose a novel scheduling scheme that uses the bit-reversal permutation sequence (BRPS) – a finite integer sequence that positions receiving wakeup slots as evenly as possible to reduce the expected sleep latency. At the same time, the sequence serves as a compact representation of wakeup schedules thereby reducing storage and communication overhead. But while low latency wakeup schedule can reduce per-hop delay in ideal conditions, it does not necessarily lead to low latency end-to-end paths because wireless link quality also plays a significant role in the performance of packet forwarding. We therefore formulate expected transmission delay (ETD), a metric that simultaneously considers sleep latency and wireless link quality. We show that the metric is left-monotonic and left-isotonic, proving that its use in distributed algorithms such as the distributed Bellman–Ford yields consistent, loop-free and optimal paths. We perform extensive simulations using real-world energy harvesting traces to evaluate the performance of the scheduling and forwarding scheme.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

Four-dimensional Markov chain model of single-hop data aggregation with IEEE 802.15.4 in wireless sensor networks

This paper introduces a new 4D Markov chain model for IEEE 802.15.4 wireless transmission, which corrects and extends an existing 3D model, providing more accurate and comprehensive results. It also introduces an analytical technique for calculating both the pdf and mean of the number of timeslots required to complete all transmissions, when a set of nodes contend for the channel at the beginning of a superframe. It is assumed that transmission takes place in beacon mode but without acknowledgement (NACK mode). The model can be used to determine the optimum value of the MAC attribute macSuperframeOrder (SO) required for saving energy, and the shortest delay required to receive all transmitted packets with a specified probability. It can also specify an upper threshold on the number of nodes and the packet length required, in order to achieve acceptable end-to-end delay. The potential creation of a traffic model for the aggregated data generated by the coordinating node is also discussed.     

EMS: Efficient mobile sink scheduling in wireless sensor networks

Sink scheduling, in the form of scheduling multiple sinks among the available sink sites to relieve the level of traffic burden, is shown to be a promising scheme in wireless sensor networks (WSNs). However, the problem of maximizing the network lifetime via sink scheduling remains quite a challenge since routing issues are tightly coupled. Previous approaches on this topic either suffer from poor performance due to a lack of joint considerations, or are based on relaxed constraints. Therefore, in this paper, we aim to fill in the research blanks. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with the placement of sinks. This bounding technique transforms the problem from time domain into pattern domain, and thus, significantly decreases the problem complexity. Then, we formulate this optimization in a pattern-based way and create an efficient Column Generation (CG) based approach to solve it. Simulations not only demonstrate the efficiency of the proposed algorithm but also substantiate the importance of sink mobility for energy-constrained WSNs.     

Modeling and designing efficient data aggregation in wireless sensor networks under entropy and energy bounds

Sensor networks are characterized by limited energy, processing power, and bandwidth capabilities. These limitations become particularly critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is considered to be an effective technique to reduce energy consumption and prevent congestion in wireless sensor networks. In this paper, we derive a number of important insights concerning the data aggregation process, which have not been discussed in the literature so far. We first estimate the conditions under which aggregation is a costly process in comparison to a non aggregation approach, by considering a realistic scenario where the processing costs related to aggregation of data are not neglected. We also consider that aggregation should preserve the integrity of data, and therefore, the entropy of the correlated data sent by sources can be considered in order to both decrease the amount of redundant data forwarded to the sink and perform an overall lossless process. We also derive the cumulative and the probability distribution functions of the delay in an aggregator node queue, which can be used to relate the delay to the amount of aggregation being considered. The framework we present in this paper serves to investigate the tradeoff between the increase in data aggregation required to reduce energy consumption, and the need to maximize information integrity, while also understanding how aggregation impacts the network propagation delay of a data packet.

An exact algorithm for maximum lifetime data gathering tree without aggregation in wireless sensor networks

In wireless sensor networks, maximizing the lifetime of a data gathering tree without aggregation has been proved to be NP-complete. In this paper, we prove that, unless P = NP, no polynomial-time algorithm can approximate the problem with a factor strictly greater than 2/3. The result even holds in the special case where all sensors have the same initial energy. Existing works for the problem focus on approximation algorithms, but these algorithms only find sub-optimal spanning trees and none of them can guarantee to find an optimal tree. We propose the first non-trivial exact algorithm to find an optimal spanning tree. Due to the NP-hardness nature of the problem, this proposed algorithm runs in exponential time in the worst case, but the consumed time is much less than enumerating all spanning trees. This is done by several techniques for speeding up the search. Featured techniques include how to grow the initial spanning tree and how to divide the problem into subproblems. The algorithm can handle small networks and be used as a benchmark for evaluating approximation algorithms.

     

Game-theoretic models for reliable path-length and energy-constrained routing with data aggregation in wireless sensor networks

Path length, path reliability, and sensor energy-consumption are three major constraints affecting routing in resource constrained, unreliable wireless sensor networks. By considering the implicit collaborative imperative for sensors to achieve overall network objectives subject to individual resource consumption, we develop a game-theoretic model of reliable, length and energy-constrained, sensor-centric information routing in sensor networks. We define two distinct payoff (benefit) functions and show that computing optimally reliable energy-constrained paths is NP-Hard under both models for arbitrary sensor networks. We then show that optimal length-constrained paths can be computed in polynomial time in a distributed manner (using O(E) messages) for popular sensor network implementations using geographic routing. We also develop sensor-centric metrics called path weakness to measure the qualitative performance of different routing schemes and provide theoretical limits on the inapproximability of computing paths with bounded weakness. Heuristics for computing optimal paths in arbitrary sensor networks are described along with simulation results comparing performance with other routing algorithms.