Data ferries based compressive data gathering for wireless sensor networks

The latest research progress of the theory of compressive sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data for wireless sensor networks. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our learning strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of the gathered data. Simulation results show that the proposed scheme improves the reconstruction accuracy and stability compared to the existing ones.

     

Multihop data gathering in wireless sensor networks with a mobile sink

Network performance can be improved by using a mobile sink (MS) to collect sensed data in a wireless sensor network. In this paper, we design an efficient trajectory for MS, collecting data from sensor nodes in a multihop fashion, with the aim of prolonging the network lifetime. Considering event‐driven applications, we present an approach to jointly determine the optimal trajectory for MS and data paths and transmission rates from source nodes to MS, without considering any rendezvous points. In these applications, an MS is supposed to harvest the data from source nodes in a given time‐slot. We first show that this problem is in form of a mixed integer nonlinear programming model, which is NP‐hard. Then, to achieve an approximate solution, we divide the mentioned problem into 2 simple subproblems. In fact, after determining an approximate zone for the trajectory of MS, the optimal data paths and transmission rates from source nodes to the MS are obtained through a mathematical optimization model. Finally, to illustrate the efficiency of the proposed approach, we compare the performance of our algorithm to an rendezvous point–based and also the state‐of‐the‐art approach in different scenarios.     

An incentive energy-efficient routing for data gathering in wireless cooperative networks

Because of energy-constraint, it is an attractive problem to select energy-efficient paths from source nodes to sink for data gathering in wireless ad hoc networks. Cooperative communication is a promising mechanism to reduce transmit energy in such kind of case. One of the fundamental assumptions for cooperative communication is that each node should be unselfish, responsible, and willing to forwarding data he has received. However, in energy-constrained environment, because of limited energy, each node hates participating in data transmission without any incentive and tries to avoid forwarding data (this behavior is selfish). In this paper, a utility function is proposed to stimulate nodes to behave unselfishly. We prove that it is a Nash Equilibrium when nodes work in an unselfish manner. Also, we show that the selection of forwarding nodes and relay nodes for data transmission is a NP-hard problem even when nodes behave unselfishly. A heuristic algorithm (Algorithm for Node Selection Problem, ANSP) is provided to solve this selection problem. We also prove the convergence of this algorithm. The analysis shows that this algorithm can reach the approximate performance ratio of 2?(1+α), where α is the maximal ratio of two power consumptions on two adjacent links in the network. The numerical results show that in a 100 node network, if nodes behave unselfishly, they will obtain a better utility, and more energy will be saved. The average saved energy when each node takes a selfish behavior, is 52.5% less than the average when nodes behave in an unselfish manner.     

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme (DTEER) was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems (PEGASIS) and the hybrid, energy- efficient, distributed clustering approach (HEED), the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.     

Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks

This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP.     

Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing

The high number of transmissions in sensor nodes having a limited amount of energy leads to a drastic decrease in the lifetime of wireless sensor networks. For dense sensor networks, the provided data potentially have spatial and temporal correlations. The correlations between the data of the nodes make it possible to utilize compressive sensing theory during the data gathering phase; however, applying this technique leads to some errors during the reconstruction phase. In this paper, a method based on weighted spatial-temporal compressive sensing is proposed to improve the accuracy of the reconstructed data. Simulation results confirm that the reconstruction error of the proposed method is approximately 16 times less than the closest compared method. It should be noted that due to applying weighted spatial-temporal compressive sensing, some extra transmissions are posed to the network. However, considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 12% improvement compared to the closest method in the literature.     

Lifetime maximized data gathering in wireless sensor networks using limited-order distributed source coding

This paper addresses the problem of efficient data gathering in wireless sensor networks with a complexity constrained data gathering node. Due to the complexity constraint, the data gathering node employs an asymmetric DSC that (de)compresses the data of a given node exploiting its dependency with a limited number of other nodes. This is characterized in a DSC rate allocation structure that is referred to as limited-order DSC. Within this structure, we investigate the problem of rate allocation for the nodes to maximize the network lifetime. To this end, an algorithm is proposed that is proven optimal with polynomial complexity in terms of number of network nodes. Numerical results demonstrate that the algorithm, even with limited complexity, allows for exploiting most of the achievable compression gain.     

Distributed trajectory design for data gathering using mobile sink in wireless sensor networks

Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity.     

EDGO: UAV-based effective data gathering scheme for wireless sensor networks with obstacles

Wireless Networks - One of the most important requirements for effective UAV–WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an...     

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.     

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.

     

Design and deployment of a smart system for data gathering in aquaculture tanks using wireless sensor networks

The design of monitoring systems for marine areas has increased in the last years. One of the many advantages of wireless sensor networks is the quick process in data acquisition. The information from sensors can be processed, stored, and transmitted using protocols efficiently designed to energy saving and establishing the fastest routes. The processing and storing of data can be very useful for taking intelligent decisions for improving the water quality. The monitoring of water exchange in aquaculture tanks is very important to monitor the fish welfare. Thus, this paper presents the design, deployment, and test of a smart data gathering system for monitoring several parameters in aquaculture tanks using a wireless sensor network. The system based on a server is able to request and collect data from several nodes and store them in a database. This information can be postprocessed to take efficient decisions. The paper also presents the design of a conductivity sensor and a level sensor. These sensors are installed in several aquaculture tanks. The system was implemented using Flyport modules. Finally, the data gathering system was tested in terms of consumed bandwidth and the delay Transmission Control Protocol (TCP) packets delivering data from the sensors.     

An image processing inspired mobile sink solution for energy efficient data gathering in wireless sensor networks

Wireless Networks - This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main...     

Optimal physical carrier sense in wireless networks

We investigate the problem of maximizing Medium Access Control (MAC) throughput in Carrier Sense Multiple Access (CSMA) wireless networks. By explicitly incorporating the carrier sense threshold and the transmit power into our analysis, we derive an analytical relation between MAC throughput and system parameters. In homogeneous networks, we derive the optimal carrier sense range at a given node density as a function of the ratio between the transmit power and the carrier sense threshold. The obtained optimal carrier sense range is smaller than that for covering the entire interference range, which is in sharp contrast to what has been considered to be optimal in previous studies. Only when the node density goes to infinity, the optimal carrier sense range converges to that for exactly covering the interference range, thereby eliminating all the hidden nodes. For nonhomogeneous networks, any distributed algorithm for tuning the carrier sense threshold, in which each node tries to maximize its own throughput without coordination, may significantly degrade MAC throughput. In order to properly design a distributed algorithm, each node not only considers its own throughput, but also needs to take account of its adverse impact on others. Our analysis is verified by simulation studies under various network scenarios.     

Quality-of-service mechanisms in all-IP wireless access networks

In this paper, we focus on resource reservation protocol (RSVP)-based quality-of-service (QoS) provisioning schemes under Internet protocol (IP) micromobility. We consider QoS provisioning mechanisms for on-going RSVP flows during handoff. First, the rerouting of RSVP branch path at a crossover router (CR) at every handoff event can minimize resource reservation delays and signaling overheads, and in turn the handoff service degradation can be minimized. We show that RSVP branch path rerouting scheme could give a good tradeoff between the resource reservation cost and the link usage. Second, the new RSVP reservation can be made along the branch path toward the CR via a new base station in advance, while the existing reservation path is maintained, and in turn the on-going flow can be kept with the guaranteed QoS. We also show that seamless switching of RSVP branch path could provide the QoS guarantee by adaptively adjusting the pilot signal threshold values. Third, during RSVP resource reservation over wireless link, dynamic resource allocation scheme is used to give a statistical guarantee on the handoff success of on-going flows. We finally obtain the forced termination probability of guaranteed service flows, the average system time of best effort flows by using a transition rate matrix approach.     

QoS routing through alternate paths in wireless ad hoc networks

Quality of service (QoS) routing plays an important role in QoS provisioning for mobile ad hoc networks. This work studies the issue of route selection subject to QoS constraint(s). Our method searches for alternate routes with satisfied QoS requirement(s) to accommodate each communication request when the shortest path connecting the source–destination pair of the request is not qualified. In order to effectively reduce protocol overhead, a directed search mechanism is designed to limit the breadth of the searching scope, which aims at achieving a graceful tradeoff between the success probability in QoS route acquisition and communication overhead. Efficient hop‐by‐hop routing protocols are designed for route selection subject to delay and bandwidth constraint, respectively. Simulation results show that the designed protocols can achieve high performance in acquiring QoS paths and in efficient resource utilization with low control overhead. Copyright © 2004 John Wiley & Sons, Ltd.     

机会移动传感器网络中的自适应数据收集机制

针对多种类型数据具有不同传输质量需求的信息采集应用,提出了一种自适应的数据收集机制.每个节点关联一个持续变化的"转发概率"参数,每个消息关联一个动态调整的"重要因子"参数,这2个参数自适应确定消息在网络中的复制次数,使数据收集机制既能满足数据传输性能的要求,又具有小的网络开销.模拟实验结果验证了该收集机制在传输性能和网络开销之间达到很好折衷.     

Network lifetime maximization for time‐sensitive data gathering in wireless sensor networks with a mobile sink

With the advances of more and more mobile sink deployments (e.g., robots and unmanned aerial vehicles), mobile sinks have been demonstrated to play an important role in the prolongation of network lifetime. In this paper, we consider the network lifetime maximization problem for time‐sensitive data gathering, which requires sensing data to be sent to the sink as soon as possible, subject to several constraints on the mobile sink. Because the mobile sink is powered by petrol or electricity, its maximum travel distance per tour is bounded. The mobile sink's maximum moving distance from its current location to the next must also be bounded to minimize data loss. As building a new routing tree rooted at each new location will incur an overhead on energy consumption, the mobile sink must sojourn at each chosen location at least for a certain amount of time. The problem, thus, is to find an optimal sojourn tour for the mobile sink such that the network lifetime is maximized, which is subject to a set of constraints on the mobile sink: its maximum travel distance, the maximum distance of each movement, and the minimum sojourn time at each sojourn location. In this paper, we first formulate this novel multiple‐constrained optimization problem as the distance‐constrained mobile sink problem for time‐sensitive data gathering. We then devise a novel heuristic for it. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the performance of the proposed algorithm is very promising, and the solution obtained is fractional of the optimal one. Copyright © 2011 John Wiley & Sons, Ltd.