Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing

Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.     

Budget-Aware Dynamic Incentive Mechanism in Spatial Crowdsourcing

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.     

移动机会网络组播路由的研究进展

在移动机会网络中,节点进行机会式的连接通信,该方式被广泛应用于移动社交网络、车载智能网络、移动自组网络等场景中。移动机会网络群组通信在抢险救灾、社区消息分发及智能交通等领域具有极高的应用价值,组播路由技术是其关键技术之一。将移动机会网络组播路由分为传统组播路由和智能组播路由两类,重点介绍了智能组播路由的研究现状,并采用通用的性能指标对各组播路由策略进行了比较分析。分析结论为:智能组播路由由于考虑了移动机会网络的社会特性、节点缓存、能量及安全等特性,因此具有较好的综合性能。最后展望了组播路由在大数据缓存管理、群组安全策略、车载自组网及动态感知方面的研究前景。     

Crowdsourcing roles,methods and tools for data-intensive disaster management

Mobile technologies, web-based platforms, and social media have transformed the landscape of disaster management by enabling a new generation of digital networks to produce, process, and analyse georeferenced data in real time. This unprecedented convergence of geomobile technologies and crowdsourcing methods is opening up multiple forms to participate in disaster management tasks. Based on empirical research, this paper first proposes a conceptualisation of crowdsourcing roles and then analyses methods and tools based on a combination of two variables: (i) types of data being processed; (ii) involvement of the crowds. The paper also surveys a number of existing platforms and mobile apps leveraging crowdsourcing in disaster and emergency management with the aim to contribute to the discussion on the advantages and limits of using crowdsourcing methods and tools in these areas.     

Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks

This paper investigates a class of mobile wireless sensor networks that are unconnected most of the times; we refer to them as delay-tolerant wireless sensor networks (DTWSN). These networks inherit their characteristics from both delay tolerant networks (DTN) and traditional wireless sensor networks. After introducing DTWSNs, three main problems in the design space of these networks are discussed: routing, data gathering, and neighbor discovery. A general protocol is proposed for DTWSNs based on opportunistic broadcasting in delay-tolerant networks with radio device on-off periods. Three performance measures are defined in the study: the energy for sending queries to ask for data from possible neighbors (querying energy), data transfer energy, and absorption time (delay). A simple yet accurate approximation for the data-transfer energy is proposed. An analytic model is provided to evaluate the querying energy per contact (epc). Simulation results for the data propagation delay show that the querying energy per contact measure obtained from the analytic model is proportional to the product of the querying energy and the delay. A practical rule of thumb for an optimal query interval in terms of delay and energy is derived from different parts of the study.     

动态传感器网络区域受限的移动sink路径选择研究

针对sink区域受限及节点特征参数的问题,如何规划sink路径选择以满足动态传感器网络高效数据收集及低能耗的要求,提出了一种动态传感器网络区域受限的移动sink路径选择方法。该方法在缓存节点辅助通信模式下,建立sink受限区域图模型。针对不同应用情况,分别讨论了sink移动全局路径信息已知和sink移动局部路径信息已知这两种情况下的最优移动路径。在全局路径信息已知时,采用Vornon单元划分的思想求解总传输能耗和节点平均负载;在局部路径信息已知时,采用启发式策略进行路径寻优,并证明其路径寻优的正确性。最后通过仿真实验与理论计算来验证移动sink最佳路径寻优策略的有效性和可行性。     

基于声誉的移动众包系统的在线激励机制

在大数据环境下,对移动众包系统的研究已经成为移动社会网络（MSN）的研究热点。然而由于网络个体的自私性,容易导致移动众包系统的不可信问题,为了激励个体对可信策略的选取,提出一种基于声誉的移动众包系统的激励机制——RMI。首先,结合演化博弈理论和生物学中的Wright-Fisher模型研究移动众包系统的可信演化趋势;在此基础上,分别针对free-riding问题和false-reporting问题建立相应的声誉更新方法,从而形成一套完整的激励机制,激励感知用户和任务请求者对可信策略的选取;最后通过模拟实验对提出的激励机制的有效性和适应性进行了验证。结果显示,与传统的基于社会规范的声誉更新方法相比,RMI有效地提高了移动众包系统的可信性。     

Analyzing multi-hop routing feasibility for sensor data harvesting using mobile sinks

This paper presents a modeling framework for characterizing the feasibility and impacts of multi-hop packet routing in sensor networks with mobile sinks. Data collection in sensor networks using mobile sinks has recently been investigated to improve energy performance at the cost of collection delay. Although the data collection can be accomplished with varying degrees of multi-hop routing, for a given data generation rate, as the extent of multi-hop routing increases, the round traversal time of the sink decreases. At the same time, the interference experienced by the mobile sink-to-sensor links and the consequent upload time go up. This paper characterizes these competing effects and develops a methodology for determining the extent of multi-hop routing that is feasible for given network and application parameters such as sensor data generation rate, wireless link capacity between sensors and mobile sink, the speed of the mobile sink and node density.     

稀疏水下传感网中AUV数据移动收集技术研究

由于水下传感器节点的水声通信距离有限、价格昂贵,水下传感器网络中一般采用稀疏方式部署,因此很难保证整体网络的连通性及数据采集效率。自主水下航行器AUV（Autonomous Underwater Vehicle）作为天然的移动数据采集平台,可以弥补固定Sink节点数据采集方式的缺陷。提出了一种基于移动AUV的水下传感网移动数据收集机制。以AUV覆盖区域内的传感器节点作为临时Sink节点,其他传感器节点以临时Sink节点为根节点,采用最小生成树MST（Minimum Spanning Tree）方法将传感数据传输到这些临时Sink节点,然后通过临时Sink节点将汇聚数据传输给AUV。随着AUV的自主移动轨迹,水下传感网的传感数据都能简单高效地被收集起来。仿真结果验证了该方法在保证网络能耗的前提下提高了数据采集效率。     

Scenes: Abstracting interaction in immersive sensor networks

Pervasive computing deployments are increasingly using sensor networks to build instrumented environments that provide local data to immersed mobile applications. These applications demand opportunistic and unpredictable interactions with local devices. While this direct communication has the potential to reduce both overhead and latency, it deviates significantly from existing uses of sensor networks that funnel information to a static central collection point. This pervasive computing driven perspective demands new communication abstractions that enable the required direct communication among mobile applications and sensors. This paper presents the scene abstraction, which allows immersed applications to create dynamic distributed data structures over the immersive sensor network. A scene is created based on application requirements, properties of the underlying network, and properties of the physical environment. This paper details our work on defining scenes, providing an abstract model, an implementation, and an evaluation.     

基于移动机器人的无线传感器网络数据收集方法

稀疏无线传感器网络中各传感器节点距离较远,而传统的静态数据收集方法要求各传感器节点直接通信,导致网络延迟时间长,能耗高。针对该问题,提出一种基于移动机器人的无线传感器数据收集方法。该方法首先由静态节点选择与路径最短的移动机器人作为簇头,移动机器人比较一定周期内检测到的邻居节点的平均剩余能量与整个网络传感器节点平均剩余能量,根据比较结果决定其是否移动,若移动则采用范围可控的随机移动策略;当移动机器人移动到新位置时,传感器节点更新路由,选择新的移动机器人作为簇头。仿真结果表明,与传统的静态无线传感器网络数据收集方法相比,基于移动机器人的无线传感器网络数据收集方法大大降低了数据传输延迟和节点能量消耗。     

移动终端群智感知研究

随着移动终端集成了越来越多的内置传感器,移动群智感知成为近几年来的研究热点。通过对移动终端传感器感知数据的收集分析处理,用户所处情境便能被识别,还原用户所处场景,为用户提供个性化服务。文中通过归纳国内外的最新研究成果,提出了移动终端群智感知模型,并从数据处理、激励机制和群智感知应用、群智感知平台等几个方面具体归纳概括了国内外的研究趋势。文中归纳了最新的数据处理技术和群智感知应用场景,并提出了竞争和协作相辅相成的激励模式。     

基于SDMA应用的移动Sink节点的设计与实现

静态无线传感器网络(所有节点均为静止)不可避免地存在能量空洞(energy hole)、冗余覆盖(overlap)和热点(hot spot)等问题.在无线传感器网络中引入节点的移动性来解决上述问题.移动节点可以完成数据收集和转发等功能,有效地减少静态节点数据传输的跳数,节约静态节点的能量,延长网络的寿命.实现了一个基于ARM7内核的移动汇点DataTruck,具有容量大、速度快等特点.在此基础上引入SDMA(space-division multiple access)技术,在移动汇点上加入智能天线,使得该节点能同时接收同一频率上多个静态节点传输的数据.实验表明基于SDMA技术的移动汇点能高效地收集数据,并减少数据延时.     

面向移动边缘计算网络的高能效计算卸载算法

移动边缘计算（mobile edge computing,MEC）是一种高效的技术,通过将计算密集型任务从移动设备卸载到边缘服务器,使终端用户实现高带宽、低时延的目标.移动边缘计算环境下的计算卸载在减轻用户负载和增强终端计算能力等方面发挥着重要作用.考虑了服务缓存,提出一种云-边-端协同的计算卸载框架,在该框架中引入D2D （device-to-device,D2D）通信和机会网络.基于建立的模型,将计算卸载决策问题转化为一个混合整数非线性规划问题,并对无线特性和移动用户之间的非合作博弈交互制定了一个迭代机制来共同确定计算卸载方案.对提出的计算卸载算法从理论上证明了多用户计算卸载博弈模型为严格势力场博弈（exact potential game,EPG）,卸载决策可获得全网范围内的最优效益.考虑到服务器的计算资源、卸载任务数据量和任务延迟需求,提出对用户和MEC服务器之间最佳用户关联匹配算法.最后,模拟结果表明,卸载决策算法具有较快的收敛速度,并在能效方面优于其他基准算法.     

传感器网络中具有负载平衡的移动协助数据收集模式

数据收集是无线传感器网络的一个基本功能.然而,现有的数据收集模式大都是基于静止基站的网络结构,导致基站周围的节点由于担负着网络内的所有负载而快速死亡,成为网络性能的瓶颈.研究如何利用移动基站收集数据来达到负载平衡.提出了一个利用移动基站协助数据收集的模式(movement-assisted data gathering,简称MADG),它将基站移动区域设置为缓冲区,首先将数据沿最短路径传输到缓冲区内,然后在基站移动的过程中进行数据收集.证明了缓冲区位置设置在距离中心时数据传输总能耗最少,并证明了存在一个缓冲区位置使得最大节点负载最小化,进而确定了同时考虑到能源消耗和负载平衡的基站移动区域.理论分析和实验结果表明,提出的数据收集模式在很大程度上降低了网络节点的最大负载,并且减少了数据传输能源中的消耗,分别比固定基站和同类工作的最大网络负载降低95%和80%以上.     

Data collection and upload under dynamicity in smart community Internet-of-Things deployments

The Internet of Things has enabled new services to communities in many domains, e.g. smart healthcare, environmental awareness, and public safety. These services require timely and accurate event delivery, but such in-situ deployments are often limited by the coverage of sensing/communication infrastructures. In this paper we develop effective, scalable, and realistic data collection and upload solutions using mobile data collectors in community IoT systems. Specifically, we address the optimized upload planning problem, i.e. determine the optimal schedule for communication to enable timely data delivery under dynamicity in network connectivity, data characteristics/heterogeneity, and mobility. We develop a two-phase approach and associated policies, where an initial upload plan is generated offline with prior knowledge of networks and data, and a subsequent runtime adaptation alters the plan under multiple dynamics. To validate our approach, we designed and built SCALECycle, our mobile data collection platform, and deployed it in real communities in Rockville, MD and Irvine, CA. Measurements from these testbeds are used to drive extensive simulations. Experimental results indicate that compared with opportunistic operation, our two-phase approach using a judicious combination of policies can result in 30%–60% improvement in overall data utility, 30% reduction in collection delays, along with greater resilience to dynamicity and improved scalability.     

能量有效和延迟敏感的无线传感器网络数据收集协议

利用移动Sink进行数据收集是无线传感器网络数据收集的一个趋势。本文提出一种能量有效、延迟敏感的移动数据收集协议（Energy—efficient and Delay—Sensitive Data Gathering Protocol for Wireless Sensor Networks,简称EEDS）。EEDS中,移动Sink在网络中穿行,从代理节点收集传感器节点监测到的数据。为了减少数据收集的延迟,采用类TSP（Traveling Salesman Problem）的解决方法,确保移动Sink在各个代理节点中收集数据时,始终选择一条最短路径在网络中行走。模拟仿真表明,提出的数据收集协议在延长网络生命周期以及减少数据收集延迟方面都有显著的优势。     

基于轨迹受限的移动Sink低能耗数据收集协议

无线传感器网络数据收集的能耗问题一直以来都是研究的热点。本文主要研究基于移动Sink轨迹受限的数据收集协议。首先针对轨迹受限的无线传感网络提出一种通用的系统模 型,将该问题形式化为最大化降低全网总路径长度轨迹设计问题 (Maximizing total length reduction for constrained trajectory, MTRC),并证明了MTRC为NP-Hard问题;然后设计一种轨迹约束低能耗贪心算法 (Trajectory constrain of low energy consumption, TCLEC),通过 TSP近似算法设计最大化降低有效长度的Sink移动轨迹。理论分析和仿真实验结果表明,TCLEC在网络拓扑数据收集树的初始化以及优化方面是高效的,并且相对于同类基于移动Sink的无线传感网络分层数据收集方法,其能耗降低了7%左右。     

Interest spaces: A unified interest-based dissemination framework for opportunistic networks

Embedded ubiquitous computing systems (EUCS) promise to grow remarkably in the near future. For such systems, new communication technologies are constructed to cope with the increasing functional and temporal demans. Because of the ubiquitousness of mobile devices nowadays, for example, traditional publish/subscribe is no longer an adequate model for data dissemination in mobile networks. Since any node can publish content at any time, the network can get congested easily, so a dissemination paradigm where mobile nodes contribute with a fraction of their resources is needed, through the use of opportunistic networks. Furthermore, a suitable organization for data dissemination in mobile networks should be centered around interests. Thus, we propose a unified interest-based dissemination framework for opportunistic networks entitled Interest Spaces, which simplifies dissemination by just allowing applications to mark data items with certain tags, letting the framework handle the caching, routing, forwarding, and disseminating. Similarly, applications that need to subscribe to channels simply have to specify the tags they are interested in, and the framework does the rest. However, the Interest Spaces framework also allows applications to have more control over the dissemination process if they wish, by specifying various criteria and dissemination rules. In this article, we present the architecture and components of Interest Spaces. We focus on data aggregation at the context layer, showing through simulations the benefits it brings in opportunistic dissemination. We also present a real-life use case for Interest Spaces through Chatty, an opportunistic chat application.     

基于能量和链路状态的AODV路由请求转发机制研究

按需距离矢量路由算法(AODV)为移动无线自组织网络(Manet)提供了高效的、扩展性能良好的路由解决方案.然而AODV在选择路径时仅以最短路径和最快响应作为度量准则,并未考虑节点能量、负栽以及链路状态等因素,导致得到的路径并非为最优且节点能量消耗不均衡,降低了网络生存时间.在传统AODV算法基础上,提出了一个改进的路由算法,该算法在路由选择时充分考虑了节点的能量、负载以及节点间的链路状态,可以有效提高网络性能并延长网络生存时间.在NS2平台上的仿真实验结果也显示,所提算法在路由负荷、时延、分组传递率以及节能方面均优于传统AODV算法.