DSPs for energy harvesting sensors: applications and architectures

Energy harvesting from human or environmental sources shows promise as an alternative to battery power for embedded digital electronics. Digital signal processors that harvest power from ambient mechanical vibration are particularly promising for sensor networks.     

基于能量收集技术的无线协作网络中继选择

针对目前能量收集技术能够收集到的可用能量受限,导致无线协作网络中继节点处易出现能量短板的问题,为了避免整个网络因中继节点大量死亡而瘫痪,提出了一种基于能量收集技术的无线协作网络中继选择方案,即联合最大能量和最大数据传输链路的中继选择方案。首先,基于节点的能量收集状况,选出每跳中能量最大的节点进行解码转发;然后,结合每连续两跳的链路传输状态,选出与源节点和目的节点之间的数据传输信道最优者作为中继节点。结合Nakagami-m信道衰落模型,将该方案与随机选择方案、最大数据链路信道增益(MaDs)方案和基于中继-窃听链路最小信道增益(BNBF)方案进行对比分析,结果表明:在满足收集的能量足够用于下一时隙能量收集和数据传输的前提下,用于能量收集的比例越小,网络中断概率越小;联合最大能量和最大数据传输链路的中继选择方案在网络中断性能方面优于其他方案,其中断概率随信噪比的增大而减小,特别是当平均信噪比为38dB时,网络中断概率降到10^-5。     

基于ZigBee无线传感网络的人体动作信息采集平台设计

采集人体动作信息,提出了一种基于ZigBee无线传感技术的采集系统,以CC2530芯片为核心设计网络的协调器和终端节点,以MMA7361L三轴加速度传感器为采集传感器,搭建ZigBee无线采集网络,并在Visual Studio开发环境下设计上位机监控界面。介绍了ZigBee协议工作原理和节点的软硬件设计方法,并给出了上位机的软件设计。实验给出了无线传感网络节点的部分采集结果,并在上位机软件中显示加速度变化的曲线图。     

MEMS based energy harvesting for the Internet of Things: a survey

Microsystem Technologies - The Internet of Things (IoT) can manage a large number of smart wireless devices and form a networking infrastructure connected to the Internet. Traditional batteries in...     

MASACAD: a multiagent based approach to information customization

MASACAD is a multiagent information customization system that adopts the machine-learning paradigm to advise students by mining the Web. In the distributed problem-solving paradigm, systems can distribute among themselves the processes necessary to accomplish a given task. Given the number of problems that distributed processing can address, AI researchers have directed significant effort toward developing specialized problem-solving systems that can interact in their search for a solution. The multiagent-system paradigm embodies this approach.     

Dynamic traffic splitting to parallel wireless networks with partial information: A Bayesian approach

Contemporary wireless networks are based on a wide range of different technologies providing overlapping coverage. This offers users a seamless integration of connectivity by allowing to switch between networks, and opens up a promising area for boosting the performance of wireless networks. Motivated by this, we consider a networking environment in which users are able to select between the available wireless networks to minimize the mean processing times for file downloads in the presence of background traffic. The information available to the user is only the total number of jobs in each network, rather than the per-network numbers of foreground and background jobs. This leads to a complex partial information decision problem which is the focus of this paper.We develop and evaluate a Bayesian learning algorithm that optimally splits a stream of jobs that minimizes the expected sojourn time. The algorithm learns as the system operates and provides information at each decision and departure epoch. We evaluate the optimality of the partial information algorithm by comparing the performance of the algorithm with the “ideal” performance obtained by solving a Markov decision problem with full state information. To this end, we have conducted extensive experiments both numerically and in a simulation testbed with the full wireless protocol stack. The results show that the Bayesian algorithm has close to optimal performance over a wide range of parameter values.     

一种基于压电效应的振动能量收集电路

为了收集环境中的振动能量,实现传感器等低能耗器件的自供能,设计了一种可收集环境中低频机械振动能量的发电电能收集电路,通过三倍压电路将压电晶体产生的交流电压进行倍压放大,利用LTC3588-1电源管理芯片中的低噪声全波整流及高效降压转换器进行变换,获得可为传感器等低能耗器件供电的直流电压。分析与实验表明,设计的振动能量收集电路可为传感器等器件提供电能,实现微弱电能的收集。     

基于二次判断的无线多传感器跌倒监测系统

通过研究跌倒事件,设计了一种基于多传感器的穿戴式跌倒监测系统.将加速度传感器、磁传感器和压力传感器相结合,采集相互独立的实时数据,并利用阈值和表决算法进行二次判断来提高系统的跌倒识别率.系统实时监测跌倒事件,并根据报警设置提醒误判或者通知家属以得到及时救助.实验结果表明,该系统有较高的识别率和可靠性,适合应用于跌倒监测系统.     

A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks

A new method is proposed in this paper to improve Low Energy Adaptive Clustering Hierarchy (LEACH) by electing cluster heads according to the residual energy of the nodes dynamically. A sliding window is set up to adjust the electing probability and keep stable the expected number of the cluster heads using two parameters in this method, one is the initial energy information of the nodes and the other is the average energy information of those that have not already been cluster heads in the network. Meanwhile, the number of cluster heads which is fixed in the entire network lifetime in LEACH is modified to be a variable according to the number of the living nodes. Simulations show that the improvement for First Node Dies (FND) and Half of the Nodes Alive (HNA) is 41% and 36%, respectively over LEACH, 17% and 26% for Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection (LEACH-DCHS), 22% and 21% for Advanced Low Energy Adaptive Clustering Hierarchy (ALEACH).     

Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.     

Distributed energy trading with transmission cost:a Stackelberg game approach

Dear editor, Distributed generation combines different renewable energy sources at the edge of the grid,such as wind and solar power.It helps manage energy dema...     

Energy conservation in wireless sensor networks: a rule-based approach

The research reported in this paper addresses the problem of energy conservation in wireless sensor networks (WSNs). It proposes concepts and techniques to extract environmental information that are useful for controlling sensor operations, in order to enable sensor nodes to conserve their energy, and consequently prolong the network lifetime. These concepts and techniques are consolidated in a generic framework we term CASE: Context Awareness in Sensing Environments framework. CASE targets energy conservation at the network level. A subset framework of CASE, we term CASE Compact, targets energy conservation at the sensor node level. In this paper, we elaborate on these two frameworks, elucidate the requirements for them to operate together within a WSN and evaluate the applications they can be applied to for energy conservation.     

Secure data aggregation in wireless sensor networks: A watermark based authentication supportive approach

In-network processing presents a critical challenge for data authentication in wireless sensor networks (WSNs). Current schemes relying on Message Authentication Code (MAC) cannot provide natural support for this operation since even a slight modification to the data invalidates the MAC. Although some recent works propose using privacy homomorphism to support in-network processing, they can only work for some specific query-based aggregation functions, e.g. SUM, average, etc. In this paper, based on digital watermarking, we propose an end-to-end, statistical approach for data authentication that provides inherent support for in-network processing. In this scheme, authentication information is modulated as watermark and superposed on the sensory data at the sensor nodes. The watermarked data can be aggregated by the intermediate nodes without incurring any en route checking. Upon reception of the sensory data, the data sink is able to authenticate the data by validating the watermark, thereby detecting whether the data has been illegitimately altered. In this way, the aggregation–survivable authentication information is only added at the sources and checked by the data sink, without any involvement of intermediate nodes. Furthermore, the simple operation of watermark embedding and complex operation of watermark detection provide a natural solution of function partitioning between the resource limited sensor nodes and the resource abundant data sink. In addition, the watermark can be embedded in both spatial and temporal domains to provide the flexibility between the detection time and detection granularity. The simulation results show that the proposed scheme can successfully authenticate the sensory data with high confidence.     

TermID: a distributed swarm intelligence-based approach for wireless intrusion detection

With the mushrooming of wireless access infrastructures, the amount of data generated, transferred and consumed by the users of such networks has taken enormous proportions. This fact further complicates the task of network intrusion detection, especially when advanced machine learning (ML) operations are involved in the process. In wireless environments, the monitored data are naturally distributed among the numerous sensor nodes of the system. Therefore, the analysis of data must either happen in a central location after first collecting it from the sensors or locally through collaboration by viewing the problem through a distributed ML perspective. In both cases, concerns are risen regarding the requirements of this demanding task in matters of required network resources and achieved security/privacy. This paper proposes TermID, a distributed network intrusion detection system that is well suited for wireless networks. The system is based on classification rule induction and swarm intelligence principles to achieve efficient model training for intrusion detection purposes, without exchanging sensitive data. An additional achievement is that the produced model is easily readable by humans. While these are the main design principles of our approach, the accuracy of the produced model is not compromised by the distribution of the tasks and remains at competitive levels. Both the aforementioned claims are verified by the results of detailed experiments withheld with the use of a publicly available security-focused wireless dataset.     

Development of a wireless stress/strain measurement system integrated with pressure-sensitive nickel powder-filled cement-based sensors

A wireless stress/strain measurement system is developed by integrating with pressure-sensitive sensors for health monitoring of concrete structures. The pressure-sensitive stress/strain sensors are fabricated by using nickel powder-filled cement-based composite. The wireless stress/strain measurement system integrated with these sensors is tested with compressive stress/strain in the range from 0 MPa/0 μ to 2.5 MPa/311.5 μ for performance evaluation. Experimental results indicate that the electrical resistivity of pressure-sensitive nickel powder-filled cement-based stress/strain sensors changes linearly and reversibly with the compressive stress/strain, and its fractional change goes up to 42.719% under uniaxial compression. The relationship between input (compressive stress/strain) and output (the fractional change in electrical resistivity) of the wireless stress/strain measurement system integrated with pressure-sensitive sensors is Δρ = −0.16894σ/Δρ = −1336.5. The wireless stress/strain measurement system can be used to achieve a sensitivity to stress/strain of 16.894% MPa⁻¹/0.13365%μ⁻¹ (a gauge factor of 1336.5) and a stress/strain resolution of 150 Pa/0.02 μ. The newly developed wireless stress/strain measurement system integrated with pressure-sensitive nickel powder-filled cement-based sensors has such advantages as high sensitivity to stress/strain, high stress/strain resolution, simple circuit and low energy consumption.     

Static and dynamic minimum energy broadcast problem in wireless ad-hoc networks: A PSO-based approach and analysis

In this paper, we address the minimum energy broadcast (MEB) problem in wireless ad-hoc networks (WANETs). The researches in WANETs have attracted significant attentions, and one of the most critical issues in WSNs is minimization of energy consumption. In WANETs the packets have to be transported from a given source node to all other nodes in the network, and the objective of the MEB problem is to minimize the total transmission power consumption. A hybrid algorithm based on particle swarm optimization (PSO) and local search is presented to solve the MEB problem. A power degree encoding is proposed to reflect the extent of transmission power level and is used to define the particle position in PSO. We also analyze a well-known local search mechanism, r-shrink, and propose an improved version, the intensified r-shrink. In order to solve the dynamic MEB problem with node removal/insertion, this paper provides an effective simple heuristic, Conditional Incremental Power (CIP), to reconstruct the broadcast network efficiently. The promising results indicate the potential of the proposed methods for practical use.     

SOFROP: Self-organizing and fair routing protocol for wireless networks with mobile sensors and stationary actors

Wireless sensor and actor networks (WSAN) have become increasingly popular in recent years. The combined operation between sensor nodes and actors results in a major advantage compared to pure sensor networks extending the range of possible applications. One of the emerging applications is the Amazon scenario in which stationary actors are deployed at accessible points in a thick forest structure and sensor nodes are thrown in a river flowing through the forest to gather observations from unreachable areas. This unprecedented and unique setting exposes two important challenges: (a) the dynamics of the river forms a continuously varying topology of sensor nodes requiring a highly adaptive network organization and (b) the inherent features of sensor and actor nodes, combined with rapid changes in the link structure of the network requiring efficient bandwidth utilization and data transmission.In this paper, we address these challenges by introducing SOFROP, a self-organizing and fair routing protocol for WSANs. Through extensive simulations, we point out two highlights of SOFROP: the efficient lightweight routing that is optimized for fairness and the locally acting adaptive overlay network formation.     

Valuing information technology infrastructures: a growth options approach

Decisions to invest in information technology (IT) infrastructure are often made based on an assessment of its immediate value to the organization. However, an important source of value comes from the fact that such technologies have the potential to be leveraged in the development of future applications. From a real options perspective, IT infrastructure investments create growth options that can be exercised if and when an organization decides to develop systems to provide new or enhanced IT capabilities. We present an analytical model based on real options that shows the process by which this potential is converted into business value, and discuss middleware as an example technology in this context. We derive managerial implications for the evaluation of IT infrastructure investments, and the main findings are: (1) the flexibility provided by IT infrastructure investment is more valuable when uncertainty is higher; (2) the cost advantage that IT infrastructure investment brings about is amplified by demand volatility for IT-supported products and services; (3) in duopoly competition, the value of IT infrastructure flexibility increases with the level of product or service substitutability; and (4) when demand volatility is high, inter-firm competition has a lower impact on the value of IT infrastructure.     

9-11 information failures: a semiotic approach

The events leading to 11 September 2001 seem to indicate an interaction of oversights, which, in concert, compromised security. Here, we use a semiotic model to explain some of what went wrong prior to 9-11. Semiotics involves the study of signs and symbols to better understand their meaning and contextual relation. We begin with so-called information. For information to be useful, it must be necessary; and to be necessary, it must be universal in the same way as a mathematical expression is understandable by mathematicians worldwide, regardless of their native language. Current systems literature provides little that addresses what is necessary and why there is a requirement for universality in information representation and processing. Our semiotic model, adapted from the existing domain of semiotic models, provides such a universal model, defining it in five levels.