An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

A low‐cost node capture attack algorithm for wireless sensor networks

Wireless sensor networks (WSNs) have many micro devices that are easy to capture. In node capture attacks, the adversary physically captures sensors and extracts all information including key information from their memories, trying to compromise the system's security protection. However, the robust and random nature of many WSN security designs makes it difficult to compromise the system even with the capture of some sensors. In this paper, we approach WSN security from an adversarial point of view and investigate low‐cost and efficient algorithms to identify sensors in a WSN in the shortest time with the lowest cost. Instead of randomly capturing sensors, an intelligent attacker should choose the next target based on the known topology so far. Because the identification of such has been proven to be NP‐hard (non‐deterministic polynomial‐time hard), we propose to transform the problem into a set covering problem and develop a greedy minimum cost node capture attack algorithm (MCA) to lower cost of attack. Extensive simulations have been implemented to evaluate the performance of MCA and to compare it with several related schemes. It is shown that MCA lowers the cost of compromising WSNs by 16%. Copyright © 2015 John Wiley & Sons, Ltd.     

Empirical analysis of the reliability of low‐rate wireless u‐healthcare monitoring applications

In this paper, we demonstrate an empirical analysis of the reliability of low‐rate wireless u‐healthcare monitoring applications. We have considered the performance analysis of the IEEE 802.15.4 low‐rate wireless technologies for u‐healthcare applications. For empirical measurement, we considered three scenarios in which the reliability features of the low‐rate wireless u‐healthcare monitoring applications have been measured: (i) distance between sensor nodes and base station; (ii) deployment of the number of sensor nodes in a network; and (iii) data transmission by different time intervals. The experimental results show that received data are used to calculate BER and analyze the performance according to the scenarios. The BER is affected when varying the distance between sensor node and base station, the number of nodes, and time interval. Copyright © 2011 John Wiley & Sons, Ltd.     

UEEDA: Uniform and energy‐efficient deployment algorithm for wireless sensor networks

Efficient and accurate sensor deployment is a critical requirement for the development of wireless sensor networks. Recently, distributed energy‐efficient self‐deployment algorithms, such as the intelligent deployment and clustering algorithm (IDCA) and the distributed self‐spreading algorithm (DSSA), have been proposed to offer almost uniform distribution for sensor deployment by employing a synergistic combination of cluster structuring and a peer‐to‐peer deployment scheme. However, both DSSA and IDCA suffer from unnecessary movements that have arisen from an inappropriate design in partial force. To improve the performance of self‐deployment algorithms, a uniform and energy‐efficient deployment algorithm (UEEDA) is proposed in this paper. Simulation results demonstrate that the proposed UEEDA outperforms both DSSA and IDCA in terms of uniformity and algorithm convergence speed. Copyright © 2007 John Wiley & Sons, Ltd.     

Tree‐based channel assignment schemes for multi‐channel wireless sensor networks

Many sensor node platforms used for establishing wireless sensor networks (WSNs) can support multiple radio channels for wireless communication. Therefore, rather than using a single radio channel for whole network, multiple channels can be utilized in a sensor network simultaneously to decrease overall network interference, which may help increase the aggregate network throughput and decrease packet collisions and delays. This method, however, requires appropriate schemes to be used for assigning channels to nodes for multi‐channel communication in the network. Because data generated by sensor nodes are usually delivered to the sink node using routing trees, a tree‐based channel assignment scheme is a natural approach for assigning channels in a WSN. We present two fast tree‐based channel assignment schemes (called bottom up channel assignment and neighbor count‐based channel assignment) for multi‐channel WSNs. We also propose a new interference metric that is used by our algorithms in making decisions. We validated and evaluated our proposed schemes via extensive simulation experiments. Our simulation results show that our algorithms can decrease interference in a network, thereby increasing performance, and that our algorithms are good alternatives for static channel assignment in WSNs. Copyright © 2015 John Wiley & Sons, Ltd.     

Providing and finding k‐road‐coverage efficiently in wireless sensor networks

In this paper, we study k‐road‐coverage problems in wireless sensor networks (WSNs). Assume there is a 2‐dimensional area Ω with a given road map = (V,E) where E contains all road segments and V consists of all intersection points on Ω. The first question we study is about ‘sensor deployment’, i.e., how to deploy a minimum number of sensor nodes on Ω such that each path (each road segment) on is k‐covered when all sensor nodes have the same sensing range. When sensors can only be deployed in a set of discrete locations, we propose an efficient method with the approximation ratio 6 + ϵ for the special case where k = 1 and O(k) generally. If sensors can be deployed in arbitrary locations, we propose an efficient method with the approximation ratio 24 + ϵ when k = 1 and O(k) generally. The second question we study is about ‘path query’, i.e., how to find the k‐covered path or k‐support path connecting any given source/destination pair of points on the road map . Basically, given any source/destination pair of points S and D, we present two algorithms which can efficiently find a k‐covered path connecting S and D and a k‐supported path connecting S and D, respectively. Copyright © 2010 John Wiley & Sons, Ltd.     

Coverage and connectivity aware critical target monitoring for wireless sensor networks: Novel NSGA‐II–based approach

As sensor nodes have limited sensing and transmission capability, their efficient deployment takes an important role in proper monitoring of the critical targets in various applications of wireless sensor networks (WSNs). The key issues that need to be taken care during deployment are the lesser number of deployed sensors, coverage of the targets, and connectivity between the sensor nodes. In this paper, we have proposed NSGA‐II with modified dominance to solve the node deployment problem with the aforementioned three conflicting objectives. The conventional domination method is modified for better performance of the NSGA‐II. An intelligent representation of chromosome is provided. Three conflicting objectives are derived to evaluate the chromosomes. Extensive simulation on the proposed algorithm and the statistical test, and analysis of variance (ANOVA) followed by post hoc analysis are performed.     

Cross‐layer MAC design for location‐aware wireless sensor networks

In this paper, we propose an optimization of MAC protocol design for wireless sensor networks, that accounts for cross‐layering information, in terms of location accuracy for nodes and residual energy levels. In our proposed solution we encode this cross‐layer information within a decreasing backoff function in the MAC. The protocol is optimized by appropriately selecting priority window lengths, and we have shown that accurate cross‐layer information plays a crucial role in achieving an optimal performance at the MAC layer level. The estimation accuracy can be characterized spatially using a location reliability probability distribution function. We show that this distribution function greatly influences the design of the optimal backoff window parameters, and the overall throughput performance of the MAC protocol. Copyright © 2010 John Wiley & Sons, Ltd.     

A frequency‐aware data‐centric mechanism for wireless sensor networks

Wireless sensor networks (WSNs) are characterized by their low bandwidth, limited energy, and largely distributed deployment. To reduce the flooding overhead raised by transmitting query and data information, several data‐centric storage (DCS) mechanisms are proposed. However, the locations of these data‐centric nodes significantly impact the power consumption and efficiency for information queries and storage capabilities, especially in a multi‐sink environment. This paper proposes a novel dissemination approach, which is namely the dynamic data‐centric routing and storage mechanism (DDCRS), to dynamically determine locations of data‐centric nodes according to sink nodes' location and data collecting rate and automatically construct shared paths from data‐centric nodes to multiple sinks. To save the power consumption, the data‐centric node is changed when new sink nodes participate when the WSNs or some queries change their frequencies. The simulation results reveal that the proposed protocol outperforms existing protocols in terms of power conservation and power balancing. Copyright © 2009 John Wiley & Sons, Ltd.     

Reduced‐frame TDMA protocols for wireless sensor networks

Time‐division multiple‐access (TDMA) is a common medium access control paradigm in wireless sensor networks. However, in its traditional form, the TDMA‐based protocols suffer from low channel utilization and high message delay because of a long frame length needed to provide collision‐free transmissions, which is particularly damaging in dense wireless sensor networks. In this paper, we investigate the performance and the energy efficiency of a class of TDMA‐based protocols, called reduced‐frame TDMA, where every TDMA slot is augmented with a short time period dedicated for carrier sense multiple access‐based contention resolution mechanism. Because of their ability to dynamically resolve collisions caused by conflicting slot assignments, the reduced‐frame TDMA protocols can be configured with any frame length, independently of node density. In addition, we present a distributed heuristic slot assignment algorithm that minimizes interslot interference in the presence of limited number of slots per frame. The simulation results indicate that the reduced‐frame TDMA protocols significantly reduce the message delay and increase the maximum throughput without incurring significant penalty in energy efficiency compared with the traditional TDMA scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

无线传感器网络的神经网络模型

无线传感器网络是复杂的无线网络。无线传感器网络拥有大量的网络节点。网络节点是无线传感器网络的基础。为了研究复杂的无线传感器网络，采用了神经元描述了WSN的网络节点，用神经元模型表示了无线传感器网络。给出了无线待感器网络节点的神经元模型和无线传感器网络的神经网络模型，并将神经网络应用于无线传感器网络的数据融合应用。结果表明，基于神经网络的无线传感器网络研究可以使得复杂研究变得简单，利于开展WSN的深入研究。     

基于云计算的矿井无线传感网络火情远程监控系统

针对矿井实际需求情况,提出了一种基于云计算的无线传感网络火情远程监控系统,此系统包括通讯基站、无线传感器网络和云计算平台,其中,无线传感器网络通过通讯基站与云计算平台相连接。它包括用于采集煤矿安全数据的无线传感器、执行器和用于传输煤矿安全数据的无线网关。该系统具备低成本、自组织、低功耗、信息交互方便的特点,具有很好的应用前景。     

A lightweight,self‐adaptive lock gate designation scheme for data collection in long‐thin wireless sensor networks

Constrained by the physical environments, the long‐thin topology has recently been promoted for many practical deployments of wireless sensor networks (WSNs). In general, a long‐thin topology is composed of a number of long branches of sensor nodes, where along a branch each sensor node has only one potential parent node toward the sink node. Although data aggregation may alleviate excessive packet contention, the maximum payload size of a packet and the dynamically changing traffic loads may severely affect the amount of sensor readings that may be collected along a long branch of sensor nodes. In addition, many practical applications of long‐thin WSNs demand the exact sensor readings at each location along the deployment areas for monitoring and analysis purposes, so sensor readings may not be aggregated when they are collected. This paper proposes a lightweight, self‐adaptive scheme that designates multiple collection nodes, termed lock gates, along a long‐thin network to collect sensor readings sent from their respective upstream sensor nodes. The self‐adaptive lock gate designation scheme balances between the responsiveness and the congestion of data collection while mitigating the funneling effect. The scheme also dynamically adapts the designation of lock gates to accommodate the time‐varying sensor reading generation rates of different sensor nodes. A testbed of 100 Jennic sensor nodes is developed to demonstrate the effectiveness of the proposed lock gate designation scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

Energy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.     

基于无线传感器网络的路边停车检测系统设计

为了构建基于无线传感器网络的大规模、低成本、低功耗的城市路边停车检测系统,本文提出了一种停车检测传感器节点硬件及软件设计方案。该系统硬件部分利用磁阻传感器实现车辆磁信号检测．软件部分主要实现基于磁信号的车辆检测算法。实地部署了80多个传感器节点的路边停车检测系统,实际应用表明该系统车辆检测精度高,达到了设计要求。     

QoS‐aware target coverage in wireless sensor networks

Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical targets, which usually have different quality of service (QoS) constraints, i.e., different targets may need different sensing quality in terms of the number of transducers, sampling rate, etc. In this paper, we address the problem of optimizing network lifetime while capturing those diversified QoS coverage constraints in such surveillance sensor networks. We show that this problem belongs to NP‐complete class. We define a subset of sensors meeting QoS requirements as a coverage pattern, and if the full set of coverage patterns is given, we can mathematically formulate the problem. Directly solving this formulation however is difficult since number of coverage patterns may be exponential to number of sensors and targets. Hence, a column generation (CG)‐based approach is proposed to decompose the original formulation into two subproblems and solve them iteratively. Here a column corresponds to a feasible coverage pattern, and the idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. An initial feasible set of patterns is generated through a novel random selection algorithm (RSA), in order to launch our approach. Experimental data demonstrate that the proposed CG‐based approach is an efficient solution, even in a harsh environment. Simulation results also reveal the impact of different network parameters on network lifetime, giving certain guidance on designing and maintaining such surveillance sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.     

Cross‐layer design for energy efficient communication in wireless sensor networks

There is a plethora of recent research on high performance wireless communications using a cross‐layer approach in that adaptive modulation and coding (AMC) schemes at wireless physical layer are used for combating time varying channel fading and enhance link throughput. However, in a wireless sensor network, transmitting packets over deep fading channel can incur excessive energy consumption due to the usage of stronger forwarding error code (FEC) or more robust modulation mode. To avoid such energy inefficient transmission, a straightforward approach is to temporarily buffer packets when the channel is in deep fading, until the channel quality recovers. Unfortunately, packet buffering may lead to communication latency and buffer overflow, which, in turn, can result in severe degradation in communication performance. Specifically, to improve the buffering approach, we need to address two challenging issues: (1) how long should we buffer the packets? and (2) how to choose the optimum channel transmission threshold above which to transmit the buffered packets? In this paper, by using discrete‐time queuing model, we analyze the effects of Rayleigh fading over AMC‐based communications in a wireless sensor network. We then analytically derive the packet delivery rate and average delay. Guided by these numerical results, we can determine the most energy‐efficient operation modes under different transmission environments. Extensive simulation results have validated the analytical results, and indicates that under these modes, we can achieve as much as 40% reduction in energy dissipation. Copyright © 2008 John Wiley & Sons, Ltd.     

A survey on IP‐based wireless sensor network solutions

Wireless sensor networks (WSNs) are composed of thousands of smart‐sensing nodes, which capture environment data for a sink node. Such networks present new challenges when compared with traditional computer networks, namely in terms of smart node hardware constraints and very limited energy resources. Ubiquitous computing can benefit from WSNs from the perspective that sensed data can be used instead of the user without explicit intervention, turning ubiquitous computing into a reality. Internet connectivity in WSNs is highly desirable, featuring sensing services at a global scale. Two main approaches are considered: proxy based or sensor node stack based. This second approach turns sensors into data‐producing hosts also known as ‘The Internet of Things’. For years, the TCP/IP (Transmission Control Protocol/Internet Protocol) suite was considered inappropriate for WSNs, mainly due to the inherent complexity and protocol overhead for such limited hardware. However, recent studies made connecting WSNs to the Internet possible, namely using sensor node stack based approaches, enabling integration into the future Internet. This paper surveys the current state‐of‐the‐art on the connection of WSNs to the Internet, presents related achievements, and provides insights on how to develop IP‐based communication solutions for WSNs today. Copyright © 2010 John Wiley & Sons, Ltd.