Recent Advances in Real-Time Maude

This paper gives an overview of recent advances in Real-Time Maude. Real-Time Maude extends the Maude rewriting logic tool to support formal specification and analysis of object-based real-time systems. It emphasizes ease and generality of specification and supports a spectrum of analysis methods, including symbolic simulation, unbounded and time-bounded reachability analysis, and LTL model checking. Real-Time Maude can be used to specify and analyze many systems that, due to their unbounded features, such as unbounded data structures or dynamic object and message creation, cannot be modeled by current timed/hybrid automaton-based tools. We illustrate this expressiveness and generality by summarizing two case studies: (i) an advanced scheduling algorithm with unbounded queues; and (ii) a state-of-the-art wireless sensor network algorithm. Finally, we give some (often easily checkable) conditions that ensure that Real-Time Maude's analysis methods are complete, also for dense time, for object-based real-time systems. In practice, our result implies that Real-Time Maude's time-bounded search and model checking of LTL time-bounded formulas are complete decision procedures for a large and useful class of non-Zeno real-time systems that fall outside the scope of systems that can be modeled in decidable fragments of hybrid automata, including the sensor network case study discussed in this paper.     

Specification and analysis of the AER/NCA active network protocol suite in Real-Time Maude

This paper describes the application of the Real-Time Maude tool and the Maude formal methodology to the specification and analysis of the AER/NCA suite of active network multicast protocol components. Because of the time-sensitive and resource-sensitive behavior, the presence of probabilistic algorithms, and the composability of its components, AER/NCA poses challenging new problems for its formal specification and analysis. Real-Time Maude is a natural extension of the Maude rewriting logic language and tool for the specification and analysis of real-time object-based distributed systems. It supports a wide spectrum of formal methods, including: executable specification; symbolic simulation; breadth-first search for failures of safety properties in infinite-state systems; and linear temporal logic model checking of time-bounded temporal logic formulas. These methods complement those offered by network simulators on the one hand, and timed-automaton-based tools and general-purpose theorem provers on the other. Our experience shows that Real-Time Maude is well-suited to meet the AER/NCA modeling challenges, and that its methods have proved effective in uncovering subtle and important errors in the informal use case specification.

Semantics and pragmatics of Real-Time Maude

At present, designers of real-time systems face a dilemma between expressiveness and automatic verification: if they can specify some aspects of their system in some automaton-based formalism, then automatic verification is possible; but more complex system components may be hard or impossible to express in such decidable formalisms. These more complex components may still be simulated; but there is then little support for their formal analysis. The main goal of Real-Time Maude is to provide a way out of this dilemma, while complementing both decision procedures and simulation tools. Real-Time Maude emphasizes ease and generality of specification, including support for distributed real-time object-based systems. Because of its generality, falling outside of decidable system classes, the formal analyses supported—including symbolic simulation, breadth-first search for failures of safety properties, and model checking of time-bounded temporal logic properties—are in general incomplete (although they are complete for discrete time). These analysis techniques have been shown useful in finding subtle bugs of complex systems, clearly outside the scope of current decision procedures. This paper describes both the semantics of Real-Time Maude specifications, and of the formal analyses supported by the tool. It also explains the tool's pragmatics, both in the use of its features, and in its application to concrete examples.     

应用IPSO的无线传感器网络分簇路由算法

在基于分簇的无线传感器网络中,网络是通过附近传感器节点在转发信息到目的节点前进行冗余数据的融合实现节能,从而延长了网络的生命周期。但现存的算法在选择簇首节点的过程中由于忽略了邻居节点的状态信息,容易导致簇内节点过早出现盲节点的现象。进化类算法已经成功应用于许多方面,微粒群算法就是其中之一。提出了一种基于改进型微粒群算法的无线传感器网络分簇路由算法来优化分簇过程。簇首节点的选取综合考虑候选节点和邻居节点的状态信息。仿真结果表明算法的性能得到了较好的改善,并延长了网络的生命周期。     

基于粒子滤波的无线传感器网络目标跟踪算法

传感器节点的组织和路由对无线传感器网络(WSN)目标跟踪算法的性能有重大影响.为此,针对具有簇一树型网络拓扑结构的WSN,首先给出集中式粒子滤波跟踪算法(CPFTA)实现的具体步骤,然后提出一种分布式粒子滤波跟踪算法(DPFTA),构建性能评价体系,通过仿真实验给出两种跟踪算法的定量比较,结果表明DPFTA的跟踪精度稍低于CPFTA,但能大幅度减少通信开销,而且具有更小的跟踪反应时间;最后仿真分析了传感器覆盖密度和检测周值对跟踪算法性能的影响.     

统计算法选择对统计模型检测效率的影响分析

近年来,统计模型检测技术已经得到了广泛的应用,不同的统计算法对统计模型检测的性能有所影响。主要对比不同统计算法对统计模型检测的时间开销影响,从而分析算法的适用环境。选择的统计算法包括切诺夫算法、序贯算法、智能概率估计算法、智能假设检验算法及蒙特卡罗算法。采用无线局域网协议验证和哲学家就餐问题的状态可达性验证为实例进行分析,使用PLASMA模型检测工具进行验证。实验结果表明,不同的统计算法在不同的环境中对模型检测的效率有不同的影响。序贯算法适用于状态可达性性质的验证,时间性能最优;智能假设检验算法与蒙特卡罗算法适合验证复杂模型。这一结论有助于在模型检测时对统计算法的选择,从而提高模型检测的效率。     

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.     

基于锚节点连通性的移动WSN定位优化算法

为提高传统移动无线传感网络非测距方式定位算法的节点定位精度、降低算法对锚节点密度的要求,提出一种基于网络中锚节点连通性的蒙特卡洛优化定位算法,并分析了其节点定位性能.算法首先引入平均锚节点连通度的概念来评价网络锚节点连通性,然后提出根据节点实时分布情况进行采样区域划分,并实时控制移动锚节点分布,提升网络的整体定位精度.仿真结果表明,相较于传统的移动无线传感网络中基于蒙特卡洛方法的节点定位算法,所提出的算法有效提升了整体的定位精度,并有效降低了算法对于锚节点密度的要求,提升了算法节点定位性能.     

无线传感器网络的路由算法研究

无线传感器网络能够实时监测和采集网络分布区域内的各种监测对象信息,有着广泛的应用前景.设计有效的路由算法来提高通信连接性、降低能量损耗、延长网络的生命周期成为无线传感器网络的核心问题.本文对无线传感器网络的各种典型路由算法进行分类,分析了各类算法的特点.通过结合各类路由算法的设计特点,提出一种适合于大规模网络的路由算法的设计,但仍需进一步改进和完善.仿真证明了该算法的有效性.     

Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

An analytical study of central and in-network data processing for wireless sensor networks

This paper compares the performance of centralized and in-network data processing for wireless sensor networks (WSNs) under various deployment conditions on the real sensor hardware Sun SPOT from Sun Microsystems. We define several criteria to measure the quality of responses in WSN applications. Guided by an extensive experimental study, we discuss in detail the performance impacts of different deployment factors on algorithms that implement both centralized and in-network computing. Finally, performance guidelines are given to algorithm designers for WSN applications.     

基于阶次序列加权的无线传感器定位算法

针对WSN中节点的定位误差的问题,提出了一种阶次序列加权的无线传感器定位算法。首先根据节点定位中的信号传输采用Shadowing模型,其次根据锚节点建立Voronoi图,将Voronoi多边形的顶点作为参考点,然后建立参考点与锚节点之间的阶次序列,通过选择N个未知节点序列与最优序列加权估计未知节点的位置,仿真结果表明,相对于参比算法,算法不仅提高了传感器节点的定位精度,而且降低了算法的计算复杂度,在WSN节点定位方面具有较高的应用价值。     

基于传感器网络的水下声音源定位方法研究

提出一种分层结构的自组织无线传感器网络(WSN)用于水下声音源的定位研究,可以广泛应用于军事、民用监控等场景;在修正的声音源衰减模型基础上,提出一种改进的非线性最小二乘算法以及极大似然算法用于水下声音源定位;仿真试验对比研究了两种算法在不同的传感器节点以及背景噪声情况下对预估定位误差的影响;试验结果表明了这种分层结构的WSN用于水下声音源定位是可行的,同时验证了最小二乘算法以及极大似然两种算法定位的有效性。     

Biologically inspired probabilistic coverage for mobile sensor networks

Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters; the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm; the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16–32 %, necessitating further modifications in their internal mechanisms.     

OMNeT++平台上无线传感器网络仿真系统的研究*

仿真系统对于无线传感器网络尤其是大规模无线传感器网络的性能评价是必不可少的。通过仿真,人们能够在一个可控的环境里研究无线传感器网络,观察由不可预测的干扰和噪声引起的节点间的相互作用,减少投放后的网络维护工作。OMNeT++是一款开源的、基于组件的、模块化的开放网络仿真平台。在OMNeT++平台提供的网络仿真基础类库和开发环境的基础上,紧密结合无线传感器网络自身特点,从仿真模型、实现机制和应用场景等方面设计了无线传感器网络仿真系统,并在Linux操作系统上编程实现。通过实验验证,该仿真模型可以正确仿真MAC     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

基于移动代理的三维DV-Hop算法

三维空间的无线传感器网络节点定位算法研究是当前的研究热点之一。通过对现有三维定位算法的不足进行分析,将无需测距的DV-Hop算法拓展到三维空间,并在通信量、定位精度方面进行了相关改进,提出了一种基于移动代理的三维DV-Hop定位算法。仿真结果表明,所提算法能对三维环境中的传感器节点进行有效的定位,信标节点的密度和通信半径对定位误差和覆盖率的影响较小,且定位精度和覆盖率相对于其他算法有明显提高。     

基于小波树模型的CoSaMP压缩感知算法

无线传感网络存在网络带宽限制和传感器节点的能耗问题,实际应用中通常希望可以通过重构算法从采集的少量数据中还原出原始信息,压缩感知理论为上述问题提供了一个解决思路。利用压缩感知理论,对无线传感器网络中温度传感器的监测信号进行了压缩感知的应用研究。针对传统压缩采样匹配追踪(CoSaMP)算法中测量次数多、重构精度低等问题,利用信号的小波系数所形成的连通树的结构特性,提出了基于小波树模型的压缩采样匹配追踪算法。将该算法应用到无线传感器网络监测信号的压缩感知仿真实验中,与传统压缩采样匹配追踪算法的重构性能进行比较,结果表明该算法较传统压缩采样匹配追踪算法在一定范围内对无线传感器网络中的温度信号具有更好的压缩感知性能。     

Energy Efficient QoS Aware Cluster Based Multihop Routing Protocol for WSN

Wireless sensor networks (WSN) have become a hot research area owing to the unique characteristics and applicability in diverse application areas. Clustering and routing techniques can be considered as an NP hard optimization problem, which can be addressed by metaheuristic optimization algorithms. With this motivation, this study presents a chaotic sandpiper optimization algorithm based clustering with groundwater flow optimization based routing technique (CSPOC-GFLR). The goal of the CSOC-GFLR technique is to cluster the sensor nodes in WSN and elect an optimal set of routes with an intention of achieving energy efficiency and maximizing network lifetime. The CSPOC algorithm is derived by incorporating the concepts of chaos theory to boost the global optimization capability of the SPOC algorithm. The CSPOC technique elects an optimum set of cluster heads (CH) whereas the other sensors are allocated to the nearer CH. Extensive experimentation portrayed the promising performance of the CSPOC-GFLR technique by achieving reduced energy utilization, improved lifetime, and prolonged stability over the existing techniques.