Dynamic proxy tree-based data dissemination schemes for wireless sensor networks

In wireless sensor networks, efficiently disseminating data from a dynamic source to multiple mobile sinks is important for the applications such as mobile target detection and tracking. The tree-based multicasting scheme can be used. However, because of the short communication range of each sensor node and the frequent movement of sources and sinks, a sink may fail to receive data due to broken paths, and the tree should be frequently reconfigured to reconnect sources and sinks. To address the problem, we propose a dynamic proxy tree-based framework in this paper. A big challenge in implementing the framework is how to efficiently reconfigure the proxy tree as sources and sinks change. We model the problem as on-line constructing a minimum Steiner tree in an Euclidean plane, and propose centralized schemes to solve it. Considering the strict energy constraints in wireless sensor networks, we further propose two distributed on-line schemes, the shortest path-based (SP) scheme and the spanning range-based (SR) scheme. Extensive simulations are conducted to evaluate the schemes. The results show that the distributed schemes have similar performance as the centralized ones, and among the distributed schemes, the SR scheme outperforms the SP scheme.     

基于无线传感器网络汇聚传输实时性的分布式调度算法

在无线传感器网络多种应用中,各节点需要在短时间内将采集的数据传输至汇聚节点,从而形成多对一的汇聚传输。针对网络汇聚传输的实时性,提出了一种分布式的节点传输调度算法。各节点只需要根据一跳范围内的邻居信息进行传输调度。仿真和分析表明该算法可以有效避免数据碰撞,并使得完成一次全网数据收集所需要的时隙数基本在网络节点总数的1.6到1.8倍左右,比目前其他调度算法在实时性和复杂度方面更具有优势。     

Distributed semi‐adaptive compressive sensing data collection in wireless sensor networks

Recently, many researches have been conducted to exploit the compressive sensing (CS) theory in wireless sensor networks (WSNs). One of the most important goals in CS is to prolong the lifetime of WSNs. But CS may suffer from some errors during the reconstruction phase. In addition, an adaptive version of CS named Bayesian compressive sensing has been studied to improve the reconstruction accuracy in WSNs. This paper investigates these adaptive methods and identifies their associated problems. Finally, a distributed and semi‐adaptive CS‐based data collection method is proposed. The proposed method tackles the aforementioned problems. Simulation results show that considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 200% improvement compared with the Bayesian compressive sensing‐based method and outperforms other compared methods in the literature.     

基于移动sink的无线传感器网络数据采集方案

对无线传感器网络的数据采集问题进行了研究,提出了一种基于可移动sink节点的数据采集方案DCSR。DCSR包括2个阶段,第1个阶段根据传感器节点的分布情况确定一批采集点,第2个阶段使用量子遗传算法求解出经过采集点的最短回路。计算完之后,sink沿着这条回路运动,周期性进行数据采集。理论分析和仿真实验表明,DCSR的性能较好,采集的数据量也更多。     

MR-MC无线传感器网络基于森林的数据收集研究

传感器网络的部署环境以及节点自身的限制,导致传感器节点很容易出现故障并且难以维护。在基于树的数据收集过程中,节点故障或者链路拥塞会造成较高的通信时延,甚至数据丢失。针对该问题提出以森林作为路由结构进行数据收集的策略。首先提出一个建立森林的算法,然后以多棵树作为路由结构进行数据收集。理论分析和实验结果表明,提出的方法可以有效减少数据收集过程中的数据丢失,在有25个故障节点的情况下,3棵树的森林路由结构收集的数据量与基于连通支配集的路由树收集的数据量相比多55%,并且能降低数据收集的延迟。     

Capacity of data collection in randomly-deployed wireless sensor networks

Data collection is one of the most important functions provided by wireless sensor networks. In this paper, we study theoretical limitations of data collection and data aggregation in terms of delay and capacity for a wireless sensor network where n sensors are randomly deployed. We consider different communication scenarios such as with single sink or multiple sinks, regularly-deployed or randomly-deployed sinks, with or without aggregation. For each scenario, we not only propose a data collection/aggregation method and analyze its performance in terms of delay and capacity, but also theoretically prove whether our method can achieve the optimal order (i.e., its performance is within a constant factor of the optimal). Particularly, with a single sink, the capacity of data collection is in order of \Uptheta(W)\Uptheta(W) where W is the fixed data-rate on individual links. With k regularly deployed sinks, the capacity of data collection is increased to \Uptheta(kW)\Uptheta(kW) when k=O(\fracnlogn)k=O\left({\frac{n}{\log n}}\right) or \Uptheta(\fracnlognW)\Uptheta\left({\frac{n}{\log n}}W\right) when k=\Upomega(\fracnlogn)k=\Upomega\left({\frac{n}{\log n}}\right). With k randomly deployed sinks, the capacity of data collection is between \Uptheta(\fracklogkW)\Uptheta\left({\frac{k}{\log k}}W\right) and \Uptheta(kW)\Uptheta(kW) when k=O(\fracnlogn)k=O\left({\frac{n}{\log n}}\right) or \Uptheta(\fracnlognW)\Uptheta\left({\frac{n}{\log n}}W\right) when k=w(\fracnlogn)k=\omega\left({\frac{n}{\log n}}\right). If each sensor can aggregate its receiving packets into a single packet to send, the capacity of data collection with a single sink is also increased to \Uptheta(\fracnlognW)\Uptheta\left({\frac{n}{\log n}}W\right).     

Locally scheduled packet bursting for data collection in wireless sensor networks

In wireless sensor networks, the many-to-one data communication pattern induces high collision losses as multiple transmissions cause contention and interference along the paths from sources to the sink. This paper proposes a low-overhead MAC layer solution to address the high contention problem to improve system throughput and reduce energy consumption. Periods of burst transmissions with reduced contention from neighboring nodes are exploited to efficiently clear up backlogged queues and improve the performance of CSMA. Through analytical modeling we characterize the expected performance improvement. Using extensive simulations on ns-2 and experiments on the 49-node sensor network testbed (Kansei) running TinyOS, we show that the proposed scheme can increase the throughput by up to a factor of four.     

Mapping density to bandwidth in tree-based wireless sensor networks

Sensors in a wireless sensor network (WSN) are often resource constrained: they are limited in processing speed, storage capacity and energy. Therefore, most of the research projects in WSN domain are aiming at the energy issue, while leaving some equally crucial issues unexplored, like the QoS support. In this paper, we present QoS support in WSN while highlighting the QoS mapping issue, a complex process in which QoS parameters are translated from level to level and we present a case study of a TDMA tree-based clustered WSN, where network density at the user level is mapped to bandwidth at the network level. We end our paper with simulations that prove our formulas and highlight the relationships between QoS parameters.     

Opportunistic data collection for disconnected wireless sensor networks by mobile mules

This paper considers a field with a number of isolated wireless sensor networks served by some mobile mules and base stations (BSs). Sensing data needs to be carried by mobile mules to BSs via opportunistic contact between them. Also, such contact may not be frequent. Thus there are four types of communications in this environment: (i) inter-node communications within a WSN, (ii) opportunistic WSN-to-mule communications, (iii) opportunistic mule-to-mule communications, and (iv) opportunistic mule-to-BS communications. In such disconnected WSNs, since sensors’ memory spaces are limited and data collection from isolated WSNs to mules and then to BSs relies on opportunistic communications in the sense that contact between these entities is occasional, storing and collecting higher-priority data is necessary. Therefore, there are two critical issues to be addressed: the data storage management in each isolated WSN and opportunistic data collection between these entities. We address the storage management problem by modeling the limited memory spaces of a WSN’s sensor nodes as a distributed storage system. Assuming that there is a sink in the WSN that will be visited by mobile mules occasionally, we address three issues: (i) how to buffer sensory data to reduce data loss due to a shortage of storage spaces, (ii) if dropping of data is inevitable, how to avoid higher-priority data from being dropped, and (iii) how to manage the data nearby the sink to facilitate the downloading jobs of mules when the downloading time is unpredictable. We propose a Distributed Storage Management (DSM) strategy based on a novel shuffling mechanism similar to heap sort. It allows nodes to exchange sensory data with neighbors efficiently in a distributed manner. For the opportunistic data collection problem, based on a utility model, we then develop an Opportunistic Data Exchange (ODE) strategy to guide two mules to exchange data that would lead to a higher reward. To the best of our knowledge, this is the first work addressing distributed storage strategy for isolated WSNs with opportunistic communications using mobile mules. We conduct extensive simulations to investigate the merit of DSM and ODE. The simulation results indicate that the level of data importance collected by our DSM is very close to a global optimization and our ODE could facilitate delivery of important data to BSs through mules. We also implement these strategies in a real sensor platform, which demonstrates that the simple and lightweight protocols can achieve our goals.     

MR-MC无线传感器网络最小延迟数据聚集调度研究

单radio单信道无线传感器网络的最小延迟聚集调度是一个NPC问题,已提出许多解决方案。在多radio多信道网络中,节点可以同时接收多个不同节点传输的数据,降低延迟。基于上述特点,考虑树结构约束,时槽、信道和radio分配等约束条件,将多radio多信道无线传感器网络最小延迟聚集调度问题定义为一个优化问题,并分解为建立聚集树和节点调度2个子问题,针对这2个子问题分别提出启发式算法。实验结果表明,提出的算法具有良好的性能。     

Near optimal scheduling of data aggregation in wireless sensor networks

Due to the large-scale ad hoc deployments and wireless interference, data aggregation is a fundamental but time consuming task in wireless sensor networks. This paper focuses on the latency of data aggregation. Previously, it has been proved that the problem of minimizing the latency of data aggregation is NP-hard [1]. Many approximate algorithms have been proposed to address this issue. Using maximum independent set and first-fit algorithms, in this study we design a scheduling algorithm, Peony-tree-based Data Aggregation (PDA), which has a latency bound of 15R + Δ ? 15, where R is the network radius (measured in hops) and Δ is the maximum node degree. We theoretically analyze the performance of PDA based on different network models, and further evaluate it through extensive simulations. Both the analytical and simulation results demonstrate the advantages of PDA over the state-of-art algorithm in [2], which has a latency bound of 23R + Δ ? 18.     

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.     

Fair data collection in wireless sensor networks: analysis and protocol

In general, wireless sensor networks (WSNs) consist of many sensors which transmit data to a central node, called the sink, possibly over multiple hops. This many-to-one data routing paradigm leads to nonuniform traffic distribution for the different sensors (e.g., nodes closer to the sink transfer more traffic than those farther away). In this paper, we perform an analysis of the fairness issue by presenting a tree-based WSN and derive the throughput, delay, and energy distribution for each sensor under the fairness constraint. Based on the analysis, we design our fair data collection protocol in which each node decides its media access and packet forwarding strategies in a distributed manner. Finally, we demonstrate the effectiveness of our solution through simulations. The results for the proposed protocol show the accuracy of the analysis and show that the protocol ensures the fair delivery of packets and reduces end-to-end delay. Based on the analysis, we also quantitatively determine the energy required for each of the nodes and show that a nonuniform energy distribution can maximize the network lifetime for the WSN scenario under study.     

Towards trusted and energy-efficient data collection in unattended wireless sensor networks

Wireless Networks - The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in...     

On reducing delay in mobile data collection based wireless sensor networks

In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.     

Distributed learning in wireless sensor networks

This paper discusses nonparametric distributed learning. After reviewing the classical learning model and highlighting the success of machine learning in centralized settings, the challenges that wireless sensor networks (WSN) pose for distributed learning are discussed, and research aimed at addressing these challenges is surveyed.     

Distributed self-learning scheduling approach for wireless sensor network

Energy constraints pose great challenges to wireless sensor network (WSN) with battery-powered nodes. But the reduction of energy consumption often introduces additional latency of data delivery. In this paper, a new distributed scheduling approach, self-learning scheduling approach (SSA), is presented in order to reduce energy consumption and to achieve low latency for WSN. This approach, extending the Q-learning method, enables nodes to learn continuous transmission parameter and sleep parameter through interacting with the WSN. We compare SSA with S-MAC protocol and DW-MAC protocol using simulations. The results show that the SSA can make nodes to learn the optimal scheduling policy gradually. The results under different work loads also exhibit that SSA performs much better than S-MAC protocol and DW-MAC protocol in terms of energy consumption and throughput. With regard to latency and maximum queue length, SSA also outperforms the other two MAC protocols in the scenarios, where the collision is serious and the work load is heavy.     

Double Q-learning-based adaptive trajectory selection for energy-efficient data collection in wireless sensor networks

This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.     

Low-power wake-up receiver with subthreshold CMOS circuits for wireless sensor networks

We developed a wake-up receiver comprised of subthreshold CMOS circuits. The proposed receiver includes an envelope detector, a high-gain baseband amplifier, a clock and data recovery (CDR) circuit, and a wake-up signal recognition circuit. The drain nonlinearity in the subthreshold region effectively detects the baseband signal with a microwave carrier. The offset cancellation method with a biasing circuit operated by the subthreshold produces a high gain of more than 100 dB for the baseband amplifier. A pulse-width modulation (PWM) CDR drastically reduces the power consumption of the receiver. A 2.4-GHz detector, a high-gain amplifier and a PWM clock recovery circuit were designed and fabricated with 0.18-μm CMOS process with one poly and six metal layers. The fabricated detector and high-gain amplifier achieved a sensitivity of ?47.2 dBm while consuming only 6.8 μW from a 1.5 V supply. The fabricated clock recovery circuit operated successfully up to 500 kbps.