Search

Industry 4.0 focuses on continuous interconnection services, allowing for the continuous and uninterrupted exchange of signals or information between related parties. The application of messaging protocols for transferring data to remote locations must meet specific specifications such as asynchronous communication, compact messaging, operating in conditions of unstable connection of the transmission line of data, limited network bandwidth operation, support multilevel Quality of Service (QoS), and easy integration of new devices. The Message Queue Telemetry Transport (MQTT) protocol is used in software applications that require asynchronous communication. It is a light and simplified protocol based on publish-subscribe messaging and is placed functionally over the TCP/IP protocol. It is designed to minimize the required communication bandwidth and system requirements increasing reliability and probability of successful message transmission, making it ideal for use in Machine-to-Machine (M2M) communication or networks where bandwidth is limited, delays are long, coverage is not reliable, and energy consumption should be as low as possible. Despite the fact that the advantage that MQTT offers its way of operating does not provide a serious level of security in how to achieve its interconnection, as it does not require protocol dependence on one intermediate third entity, the interface is dependent on each application. This paper presents an innovative real-time anomaly detection system to detect MQTT-based attacks in cyber-physical systems. This is an online-semisupervised learning neural system based on a small number of sampled patterns that identify crowd anomalies in the MQTT protocol related to specialized attacks to undermine cyber-physical systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is placing new demands on existing communication systems. The limited orthogonal resources do not meet the demands of massive connectivity of future IoT systems that require efficient multiple access. Interleave-division multiple access (IDMA) is a promising method of improving spectral efficiency and supporting massive connectivity for IoT networks. In a given time, not all sensors signal information to an aggregation node, but each node transmits a short frame on occasion, e.g., time-controlled or event-driven. The sporadic nature of the uplink transmission, low data rates, and massive connectivity in IoT scenarios necessitates minimal control overhead communication schemes. Therefore, sensor activity and data detection should be implemented on the receiver side. However, the current chip-by-chip (CBC) iterative multi-user detection (MUD) assumes that sensor activity is precisely known at the receiver. In this paper, we propose three schemes to solve the MUD problem in a sporadic IDMA uplink transmission system. Firstly, inspired by the observation of sensor sparsity, we incorporate compressed sensing (CS) to MUD in order to jointly perform activity and data detection. Secondly, as CS detection could provide reliable activity detection, we combine CS and CBC and propose a CS-CBC detector. In addition, a CBC-based MUD named CBC-AD is proposed to provide a comparable baseline scheme. [ABSTRACT FROM AUTHOR]

This study focuses on the differences in power consumption between beam-forming and relaying data transmission methods in a sparse wireless ad-hoc network. These two methods are observed for the same parameters using an identical network topology in a simulation programme that was developed as a part of this study. This paper introduces the reader the background of sensor networks and exposes the aims of this study and methods used to simulate these networks. Finally, the results of a simulation are analysed and the two methods are compared, followed by the conclusion of the study and the project itself. [ABSTRACT FROM AUTHOR]

In this paper, a fault diagnosis (FD) and fault tolerant control (FTC) scheme is proposed for the leader–follower based multiple manipulator system with sensor fault under network communication topology. The learning observer is designed to obtain the fault information when the time-varying sensor fault occurs in follower manipulators. The synchronization error between the leader manipulator and the follower manipulator is defined based on the graph theory. The distributed multiple manipulator synchronization controller is designed for each follower manipulator which contains the value of fault estimation and RBF neural network based approximator. Correspondingly, the stability analysis of the error system is analyzed by the Lyapunov stability theorem. Finally, a simulation example is given to prove the effectiveness of the algorithm. • The learning observer is designed to obtain the sensor fault information. • The manipulator synchronization FTC scheme is designed for the follower manipulator. • The FTC controller can reduce the impact of the sensor fault. [ABSTRACT FROM AUTHOR]

Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a "Differential Characteristics-Based Cyclic Prefix (DC-CP)" detector and a "Differential Characteristics-Based Pilot Tones (DC-PT)" detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors. [ABSTRACT FROM AUTHOR]

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process. [ABSTRACT FROM AUTHOR]

The authors reflect on the importance of body-centric wireless communications system which is commonly used in electronic data processing. The telecommunication system has a wearable and implantable wireless sensors which perform many applications. These include personal communications, medical aspects, military services, space exploration and identification systems.

Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors' design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas (β) is close to 1, iterative matrix inversion methods are not attaining a good detector's performance. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) communications system is commercially introduced by several mobile operators where sub-6 GHz bands are the backbone of the 5G networks. A large-scale multiple-input multiple-output (MIMO), or massive MIMO (mMIMO), technology has a major impact to secure high data rate, high spectral efficiency, and quality of service (QoS). It could also have a major role in the beyond-5G systems. A massive number of antennas seek advanced signal processing to detect and equalize the signal. However, optimal detectors, such as the maximum likelihood (ML) and maximum posterior (MAP), are not desirable in implementation due to extremely high complexity. Therefore, sub-optimum solutions have been introduced to obtain and guarantee enough balance between the performance and the computational complexity. In this paper, a robust and joint low complexity detection algorithm is proposed based on the Jacobi (JA) and Gauss–Seidel (GS) methods. In such iterative methods, the performance, complexity, and convergence rate are highly dependent on the initial vector. In this paper, initial solution is proposed by exploiting the benefits of a stair matrix to obtain a fast convergence rate, high performance, and low complexity. Numerical results show that proposed algorithm achieves high accuracy and relieve the computational complexity even when the BS-to-user-antenna ratio (BUAR) is small. [ABSTRACT FROM AUTHOR]

Today, due to the impossibility of further reducing the dimensions, electronic devices are faced with fundamental challenges in parameters such as speed, frequency, and power consumption for CMOS technology circuit. One solution is to replace CMOS technology with other technologies such as Quantum-dot Cellular Automata (QCA) technology. Extensive researches have been done for design digital circuits in QCA technology. Phase-frequency detector (PFD) is one of the main blocks in electrical and communication systems. In this paper, a structure is presented for a PFD in QCA technology for the first time. In the proposed structure, a D flip-flop (D-FF) with reset ability based on a new inverter gate is used. The new inverter gate of this D-FF compared to previous inverters, has output signal with high polarization level. Also, the proposed PFD has 199 cells, 0.22 μm2 occupied area and two clock cycles latency that is smaller compared with PFD which is designed with the conventional inverter. [ABSTRACT FROM AUTHOR]

Signal acquisition and sensing in underwater systems and applications is typically a challenging issue due to the small signal strength within the background noise. Here, we present a ring vector sensor communication receiver that can significantly improve signal acquisition, by utilizing the underwater acoustic vector field components, compared to the scalar component. The vector sensor receiver is a multichannel receiver that measures particle velocities, which are vector components of the underwater acoustic field, in addition to the scalar field component. According to the combination of our measured experimental data with our signal acquisition performance analysis, the introduced ring vector sensor receiver exhibits higher signal acquisition probabilities for the vector components compared to the scalar component. This can be attributed to certain characteristics of the vector field components. Another advantage of this multichannel receiver is that combining all of its channels can further increase the signal acquisition and packet detection probability in underwater communication systems compared to a single-channel approach. [ABSTRACT FROM AUTHOR]

In this paper, we present Esense, a new paradigm of communication between devices that have fundamentally different physical layers. Esense is based on sensing and interpreting energy profiles. While our ideas are generic enough to be applicable in a variety of contexts, we illustrate the usefulness of our ideas by presenting novel solutions to existing problems in three distinct research domains. As part of these solutions, we demonstrate the ability to communicate between devices that follow two different standards: IEEE 802.11 and 802.15.4. We consider two scenarios here: 1) where there is no background traffic and 2) where there is background 802.11 traffic. In each case, we build an "alphabet setâ: a set of signature packet sizes that can be used for Esense communication. Specifically for the second case, we take a measurement-based alphabet set construction by considering WiFi traces from actual deployments. Based on practical observations and experiments, we theoretically quantify the maximum achievable transmission rate when using Esense. With background traffic, we could potentially construct an alphabet of size as high as 100. Such a large alphabet size promises efficient Esense communication. We show via a prototype implementation that effective communication is indeed feasible even when both sides use different physical layers. [ABSTRACT FROM AUTHOR]

Today, no simple methods are available for measurements of interference signals that give a proper indication of the impact in terms of the bit error probability (BEP) on a digital radio receiver. Such measure should quantify the corresponding impact and the measurements should be relatively easy to perform. By only using the root mean square (RMS) value of the interference average power the BEP can be underestimated with several magnitudes. To address this problem, an impulsiveness correction factor (ICF) has earlier been proposed to adjust for these errors. The ICF opens up for considerably more accurate BEP estimations whereas the simplicity in the calculations is maintained. However, how to determine the ICF for an arbitrary interference source through measurements has not earlier been known. In this paper, we show that the ICF can be obtained in two alternative ways. One way is to use the measured amplitude probability distribution (APD). The other way is to use the peak- and RMS values from standard measurement detectors. Both methods take the interference waveform properties into consideration and the BEP can thus be more accurately estimated. [ABSTRACT FROM PUBLISHER]

The use of multiple frequency shift keying modulation with permutation codes addresses the problem of permanent narrowband noise disturbance in a power line communications system. In this paper, we extend this coded modulation scheme based on permutation codes to general codes and introduce an additional new parameter that more precisely captures a code's performance against permanent narrowband noise. As a result, we define a new class of codes, namely, equitable symbol weight codes, which are optimal with respect to this measure. [ABSTRACT FROM AUTHOR]

Consider mobile targets in a plane and their movements being monitored by a network such as a field of sensors. We develop distributed algorithms for in-network tracking and range queries for aggregated data (for example, returning the number of targets within any user given region). Our scheme stores the target detection information locally in the network and answers a query by examining the perimeter of the given range. The cost of updating data about mobile targets is proportional to the target displacement. The key insight is to maintain in the sensor network a function with respect to the target detection data on the graph edges that is a differential form such that the integral of this form along any closed curve C gives the integral within the region bounded by C. The differential form has great flexibility, making it appropriate for tracking mobile targets. The basic range query can be used to find a nearby target or any given identifiable target with cost O(d), where d is the distance to the target in question. Dynamic insertion, deletion, coverage holes, and mobility of sensor nodes can be handled with only local operations, making the scheme suitable for a highly dynamic network. It is extremely robust and capable of tolerating errors in sensing and target localization. Targets do not need to be identified for the tracking, thus user privacy can be preserved. In this paper, we only elaborate the advantages of differential forms in tracking of mobile targets. Similar routines can be applied for organizing many other types of information—for example, streaming scalar sensor data (such as temperature data field)—to support efficient range queries. We demonstrate through analysis and simulations that this scheme compares favorably to existing schemes that use location services for answering aggregate range queries of target detection data. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are more likely to be distributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an \cal R0-proper carrier-sensing range (\cal R0-PCR) under the generalized physical interference model, where \cal R0 is the satisfied threshold of data receiving rate. Taking \cal R0-PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on \cal R0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply \cal R0-PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA. [ABSTRACT FROM AUTHOR]

We develop several novel signal detection algorithms for two-dimensional intersymbol-interference channels. The contribution of the paper is two-fold: (1) We extend the one-dimensional maximum a-posteriori (MAP) detection algorithm to operate over multiple rows and columns in an iterative manner. We study the performance vs. complexity trade-offs for various algorithmic options ranging from single row/column non-iterative detection to a multi-row/column iterative scheme and analyze the performance of the algorithm. (2) We develop a self-iterating 2-D linear minimum mean-squared based equalizer by extending the 1-D linear equalizer framework, and present an analysis of the algorithm. The iterative multi-row/column detector and the self-iterating equalizer are further connected together within a turbo framework. We analyze the combined 2-D iterative equalization and detection engine through analysis and simulations. The performance of the overall equalizer and detector is near MAP estimate with tractable complexity, and beats the Marrow Wolf detector by about at least 0.8 dB over certain 2-D ISI channels. The coded performance indicates about 8 dB of significant SNR gain over the uncoded 2-D equalizer-detector system. [ABSTRACT FROM AUTHOR]

Increasing the lifetime of a wireless sensor network (WSN) while maintaining good coverage is a well studied research problem. A cellular automaton is a biologically inspired model that has been widely used to model different physical systems including wireless sensor networks. In this paper, we use cellular automaton algorithms to optimize the coverage and the lifetime of a WSN. An advantage of the cellular automaton model is that its algorithms rely only on limited local information that can easily be stored in individual nodes. We consider a radius 2 neighborhood instead of a radius 1 neighborhood and consider different energy levels for different operations of the network. We compare different rules of this model with the rules of the existing model (radius 1 neighborhood) and we devise a probabilistic technique to solve the sudden falls of the coverage in these models. We develop cellular automaton based algorithms also for the object detection problem and present simulation results. [ABSTRACT FROM AUTHOR]

This paper examines the impact of sensor censoring on the decision fusion performance in networks with an unknown number of sensors. In performing the decision fusion process, the fusion center applies the Chair-Varshney test; suitably modified to take account of the unknown network size. A closed-form analytical expression is derived for the error probability of the modified fusion rule. It is shown that reducing the censoring probability, i.e., allowing a greater number of sensors to transmit their decisions, does not necessarily improve the decision fusion performance. Rather, there exists a certain censoring probability threshold below which increasing the number of transmitting sensors simply incurs a greater intra-network communication overhead but without improving the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold. Hence, it is desirable that the value of this censoring probability threshold be known in advance. Accordingly, the present study proposes an efficient method for identifying the censoring probability threshold value and determining the corresponding local censoring rule. [ABSTRACT FROM AUTHOR]

Copyright of Gerencia Tecnologica Informatica is the property of Universidad Industrial de Santander and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since, current widely available network protocols/ systems are mainly throughput-oriented designs, meeting stringent delay requirements of new applications such as virtual reality and vehicle-to-vehicle communications on cellular network requires new network protocol/system designs. C2TCP is an effort toward that new design direction. C2TCP is inspired by in-network active queue management designs such as RED and CoDel and motivated by lack of a flexible end-to-end approach which can adapt itself to different applications’ QoS requirements without modifying any network devices. It copes with unique challenges in cellular networks for achieving ultra-low latency (including highly variable channels, deep per-user buffers, self-inflicted queuing delays, and radio uplink/downlink scheduling delays) and intends to satisfy stringent delay requirements of different applications while maximizing the throughput. C2TCP works on top of classic throughput-oriented TCP and accommodates various target delays without requiring any channel prediction, network state profiling, or complicated rate adjustment mechanisms. We have evaluated C2TCP in both real-world environment and extensive trace-based emulations and compared its performance with different TCP variants and state-of-the-art schemes including PCC-Vivace, Google’s BBR, Verus, Sprout, TCP Westwood, and Cubic. Results show that C2TCP outperforms all these schemes and achieves lower average delay, jitter, and 95th percentile delay for packets. [ABSTRACT FROM AUTHOR]

Abstract Molecular Communication via Diffusion (MCvD) is a promising paradigm which enables nano-machines to communicate with each other. However, the reliability of existing communication systems degrades rapidly as the distance between the transmitters and the receivers grows. To solve this issue, relaying schemes must be implemented in practice. In this paper, we study two relaying schemes: In the first case, the relay node decodes the incoming signal symbol and forwards it to the receiver using a different types of molecules. Then, the receiver detects the information bits by only considering the molecules from the relay node. In the second case, the receiver considers both types of molecules sent from the transmitter and the relay node. Closed-form expression for the end-to-end bit error probability of the these two schemes are derived. Also, we obtain optimal relay location for both schemes. Moreover, we study the optimal threshold, concentration, baud-rate and timing offset to mitigate the overall Inter-Symbol Interference(ISI) and obtain maximum throughput in the relay network. [ABSTRACT FROM AUTHOR]

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things. [ABSTRACT FROM AUTHOR]

In most of the wireless sensor and actor network applications, it is desirable to have an autonomous process for positioning the actors in order to eliminate human intervention as much as possible. For this purpose, sensors and actors deployed in an area of interest can collaborate in a distributed manner. Typically, sensors can instruct actors for their positioning in the area by considering the application level interests. This instruction is based on selecting representative sensor locations. One of the most common ways of selecting such representative locations is to cluster the network and determine cluster heads (CHs) as representatives. The actors can then move to such CH locations by talking to nearby sensors/actors. Such movement, however, should be done wisely in order to minimise the movement distance of actors so that their lifetimes can be extended. Nevertheless, this may not be possible since not all the actor and CH locations will be known by each actor. In this paper, we propose an actor-CH location matching algorithm which assigns the actors to appropriate CH locations in a distributed manner with minimised travel distance on actors and message overhead on sensors. We adapt the Gale-Shapley (GS) stable matching algorithm from stable matching theory. In this matching algorithm, actors are regarded as men and CHs are regarded as women. For distributed execution of the algorithm, sub-networks of actors and CHs are determined. Each sub-network elects a leader that performs the matching in the sub-network based on GS algorithm. If there are unmatched actors after this process, either a separate search is used to detect such nodes or leaders talk to each other to share this information so that further matching can be performed. We have evaluated the performance of our approach through simulation and shown that our approach can produce travel distance results that are very close to the brute-force approach with minimal messaging overhead. [ABSTRACT FROM AUTHOR]

Users, especially when in unfamiliar areas, would appreciate services that provide them with information about their surroundings and objects in the neighborhood. A natural way to find such information is by looking at the objects. Therefore, it would be very useful if Location-based Services could provide a kind of viewer-centric interaction between the users and their surroundings. In this paper, we outline iVu.KOM as a framework for running efficient and prompt mobile viewer-centric queries. We describe our concepts of efficiently modeling the geometry that is surrounding the user, and the modeling of the user's field of view using orientation sensors existing in emerging smartphones. Based on real-world experiments, we provide also an evaluation of the framework capabilities to recognize and react to sensors' uncertainties. [ABSTRACT FROM AUTHOR]

Exploiting high density features of wireless sensor networks represents a challenging issue. In this context, anonymous, asynchronous and randomly distributed sensors are considered along with few devices, called actors, which are more powerful than sensors in terms of energy and transmission capabilities. The paper proposes a new distributed training protocol for coarse-grain localization purposes in high density environments. The aim is to auto-organize the sensors with respect to a virtual infrastructure centered at actors and constituted of concentric rings divided into sectors. Analytical study as well as experiments on the proposed protocol are provided. The obtained results show under which theoretical and practical settings the training process can be performed in a fast and high quality way with respect to the granularity of the required localization and the energy consumption. [ABSTRACT FROM AUTHOR]

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are well suited to many applications, including environment surveillance and target tracking. ZigBee is an IEEE 802.15.4-based standard that is considered suitable for WSNs. The functional operations of a ZigBee network rely on self-organized network connections and the proper deployment of sensor devices. However, the devices comprising a ZigBee network may become isolated from the network after the joining phase due to the configuration constraints of the ZigBee standard. This means that some deployed devices cannot join the network even though they can communicate with other joined nodes. These isolated devices reduce the efficiency of network operation and increase deployment costs. This paper proposes a ZigBee-compatible adaptive joining mechanism with connection shifting schemes to improve the connectivity of ZigBee networks, allowing them to operate at the expected efficiency. Simulation results show that the proposed mechanism significantly improves the join ratio of deployed sensor devices in ZigBee WSNs. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Most wireless sensor networks (WSNs) consist of battery-powered nodes and are limited to hundreds of nodes. Battery replacement is a very costly operation and a key factor in limiting successful large-scale deployments. The recent advances in both energy harvesters and low-power communication systems hold promise for deploying large-scale wireless green-powered sensor networks (WGSNs). This will enable new applications and will eliminate environmentally unfriendly battery disposal. This paper explores the use of energy harvesters to scavenge power for nodes in a WSN. The design and implementation of a generic energy-harvesting framework, suited for a WSN simulator as well as a real-life testbed, are proposed. These frameworks are used to evaluate whether a carrier sensemultiple access with collision avoidance scheme is sufficiently reliable for use in emerging large-scale energy harvesting electronic shelf label (EHESL) systems (i.e., 12000 labels in a star topology). Both the simulator and testbed experiments yielded an average success rate up to 92%, with an arrival rate of 40 transceive cycles per second. We have demonstrated that our generic energy-harvesting framework is useful for WGSN research because the simulator allowed us to verify the achieved results on the real-life testbed and vice versa. [ABSTRACT FROM AUTHOR]

A precise localization is required in order to maximize the usage of Mobile Sensor Network. As well, mobile robots also need a precise localization mechanism for the same reason. In this paper, we showed a combination of various localization mechanisms for precise localization in three different levels. Localization can be classified in three big categories: wide area and long distance localization with low accuracy, medium area and distance localization with medium accuracy, and small area short distance localization with high accuracy. In order to present localization methods, traditional map building technologies such as grid maps or topological maps can be used. We implemented mobile sensor vehicles and composed mobile sensor network with three levels of localization techniques. Each mobile sensor vehicles act as a mobile sensor node with the facilities such as autonomous driving, obstacle detection and avoidance, map building, communication via wireless network, image processing, extensibility of multiple heterogeneous sensors, and so on. For localization, each mobile sensor vehicle has abilities of the location awareness by mobility trajectory based localization, RSSI based localization and computer vision based localization. With this set of mobile sensor network, we have the possibility to demonstrate various localization mechanisms and their effectiveness. In this paper, the result of computer vision based localization, sensor mobility trail based localization and RSSI based localization will be presented. [ABSTRACT FROM AUTHOR]

In this paper, a low complexity iterative intercarrier interference (ICI) cancellation and equalization technique is proposed for use in OFDM systems over doubly selective channels. In the iterative parallel interference cancellation/minimum mean square error (PIC/MMSE) detector has a high complexity and a restriction on the structure which can not remove the ICI in the initial stage., Therefore, an error propagation occurs due to the ICI regenerated by the incorrect output of soft-input soft-output (SISO) decoder. In order to reduce the error propagation, an MMSE detector based on the successive interference cancellation (SIC) is used in the initial stage. The low complexity MMSE detector is also derived to minimize the error propagation by quantifying the decision error before SISO decoding. In the first iteration, simulation results show that the proposed scheme outperforms the conventional PIC/MMSE scheme by about 3 dB at bit error rate (BER) = 1 × 10-3 while maintaining the equivalent computational complexity. In the subsequent iteration, it is possible to cancel the ICI out in the received signals by the aid of soft log-likelihood ratio (LLR) fed from the SISO decoder. Converting the LLR to the decision error probability, the error covariance matrix is obtained more accurately. As a result, the error propagation can be effectively reduced by dealing with only the dominant components, when considering decision errors. Finally, simulation results show that the proposed scheme outperforms the conventional PIC/MMSE scheme. [ABSTRACT FROM AUTHOR]

The K-Nearest Neighbors (KNN) query has been of significant interest in many studies and has become one of the most important spatial queries in mobile sensor networks. Applications of KNN queries may include vehicle navigation, wildlife social discovery, and squad/platoon searching on the battlefields. Current approaches to KNN search in mobile sensor networks require a certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free itinerary-based approach called Density-aware Itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme takes into account several challenging issues such as the trade-off between degree of parallelism and network interference on query response time, and the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. To optimize the performance of DIKNN, a detailed analytical model is derived that automatically determines the most suitable degree of parallelism under various network conditions. This model is validated by extensive simulations. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases. It outperforms the second runner with up to a 50 percent saving in energy consumption and up to a 40 percent reduction in query response time, while rendering the same level of query result accuracy. [ABSTRACT FROM AUTHOR]

This paper considers the coverage problem for hybrid networks which comprise both static and mobile sensors. The mobile sensors in our network only have limited mobility, i.e., they can move only once over a short distance. In random static sensor networks, sensor density should increase as O(log L + k log log L) to provide k-coverage in a network with a size of L. As an alternative, an all-mobile network can provide k-coverage with a constant density of O(k), independent of network size L. We show that the maximum distance for mobile sensors is O(1/√k log3/4(kL)). We then propose a hybrid network structure, comprising static sensors and a small fraction of O(1/√k) of mobile sensors. For this network structure, we prove that k-coverage is also achievable with a constant sensor density of O(k). Furthermore, for this hybrid structure, we prove that the maximum distance which any mobile sensor has to move is bounded as O(log3/4 L). We then propose a distributed relocation algorithm, where each mobile sensor only requires local information in order to optimally relocate itself. We verify our, analysis via extensive numerical evaluations and show an implementation of the mobility algorithm on real mobile sensor platforms. [ABSTRACT FROM AUTHOR]

Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a battlefield. The intrusion detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to characterize the WSN parameters such as node density and sensing-range in terms of a desirable detection probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, we derive the detection probability by considering two sensing models: single-sensing detection and multiple-sensing detection. In addition, we discuss the network connectivity and broadcast reachability, which are necessary conditions to ensure the corresponding detection probability in a WSN. Our simulation results validate the analytical values for both homogeneous and heterogeneous WSNs. [ABSTRACT FROM AUTHOR]

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called Congestion-Aware Routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-Enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed. [ABSTRACT FROM AUTHOR]

One of the fundamental and important operations in sensor networks is sink–source matching, i.e. target detection. Target detection is about how a sink finds the location of source nodes observing the event of interest (i.e. target activity). This operation is very important in many sensor network applications such as military battlefield and environment habitats. The mobility of both targets and sinks brings significant challenge to target detection in sensor networks. Most existing approaches are either energy inefficient or lack of fault tolerance in the environment of mobile targets and mobile sinks. Motivated by these, we propose an energy-efficient line proxy target detection (LPTD) approach in this paper. The basic idea of LPTD is to use designated line proxies as rendezvous points (or agents) to coordinate mobile sinks and mobile targets. Instead of having rendezvous nodes for each target type as used by most existing approaches, we adopt the temporal-based hash function to determine the line in the given time. Then the lines are alternated over time in the entire sensor network. This simple temporal-based line rotation idea allows all sensor nodes in the network to serve as rendezvous points and achieves overall load balancing. Furthermore, instead of network-wide flooding, interests from sinks will be flooded only to designated line proxies within limited area. The interest flooding can further decrease if the interest has geographical constraints. We have conducted extensive analysis and simulations to evaluate the performance of our proposed approach. Our results show that the proposed approach can significantly reduce overall energy consumption and target detection delay. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the advance of sensing technologies and their applications, advanced sensor networks are gaining increasing interest. For certain sensitive applications, heterogeneous sensors can be deployed in the monitored space to ensure scalability, high-speed communication, and long network lifetime. Hybrid sensor networks have capabilities to combine the use of both resource-rich and resource-impoverished sensor nodes. This paper proposes a heterogeneous broadband sensor network architecture for military vehicle tracking. Powerful sensor devices with good bandwidth and energy capabilities are used as a communication backbone while energy sensors are used to track moving targets. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper we describe the architecture and the performances of a hybrid modular acquisition and control system prototype we developed in Napoli for distributed monitoring and control systems. The system, developed by our group within the framework of R&D for interferometric detectors of gravitational waves, is based on a dual-channel 18-bit low noise ADC and 16-bit DAC module at 800 kHz, managed by an Altera FPGA. The module is designed to be used standalone or mounted as mezzanine on a motherboard, in parallel with other modules. In particular, the modules can send/receive the configuration and the acquired/correction signal for control through a standard EPP parallell port to/from an external PC, where the real-time computation is performed. Experimental tests have demonstrated that this architecture allows the implementation of distributed control systems with a sustained sampling frequency up to fc ≈80 kHz, using a standard PC for the control signals computation. Each module is also equipped with a 20-bit slow ADC necessary for the acquisition of an external calibration signal. The system is now being extensively tested in on a prototype of suspended optical interferometer for gravitational wave detection we are developing at the INFN in Napoli, made of three superattenuators of the same class of the TAMA Interferometer. [ABSTRACT FROM AUTHOR]

In this paper, we propose a receiver structure that performs joint iterative multiuser detection and channel estimation for the uplink of differentially coded asynchronous direct-sequence code-division multiple-access systems in unknown multipath channels. The overall transmission scheme for each user is the serial concatenation of a recursive systematic convolutional (RSC) encoder and a differential encoder separated by an inter- leaver. The proposed receiver consists of two stages. The first stage performs channel estimation, soft interference cancellation, and soft-input soft-output multiuser filtering, followed by the second stage, which consists of a bank of serially concatenated single-user channel decoders and differential decoders. The single-user iterative decoder for each user consists of a powerful combination of an RSC decoder and a differential decoder, which work together in an iterative fashion. In terms of channel estimation, at the first iteration, the multipath channel is blindly estimated by exploiting the orthogonality property between the signal and noise subspaces, whereas, for the next iterations, the channel is estimated using the soft estimates of coded bits of each user provided by the single-user iterative decoders in conjunction with the output of the multiuser detector. By exchanging soft information between the different stages, the receiver performance is improved through iteration. Simulation results demonstrate that the performance of the proposed iterative multiuser detector with the integrated channel estimator approaches the single-user bound at high signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques for dynamic systems are desired. In this paper, we consider fault detection in dynamic systems monitored by multiple sensors. Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent and identically distributed (IID) observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on state estimation via particle filtering. Illustrative examples are presented to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4 is a new standard that addresses the need of low-rate wireless personal area networks or LR-WPAN with a focus on enabling pervasive wireless sensor networks for residential, commercial and industrial applications. The standard is characterized by maintaining a high level of simplicity, allowing for low cost and low power implementations. This paper presents a brief technical introduction of the IEEE 802.15.4 standard and analyzes its applicability for building automation applications. [ABSTRACT FROM AUTHOR]

This paper presents a distributed control algorithm for multi-target surveillance by multiple robots. Robots equipped with sensors and communication devices discover and track as many evasive targets as possible in an open region. The algorithm utilizes information from sensors, communication, and a mechanism to predict the minimum time before a robot loses a target. Workload is shared locally between robots using a greedy assignment of targets. Across long distances robots cooperate through explicit communication. The approach is coined Behavioral Cooperative Multi-robot Observation of Multiple Moving Targets. A formal representation of the proposed algorithm as well as proofs of performance guarantee are provided. Extensive simulations confirm the theoretical results in practice. [ABSTRACT FROM AUTHOR]

Locationing problem in Wireless Sensor Networks(WSNs) can be viewed as a general distributed sensor problem. It is with sensors that can discover other nodes or estimate ranges between nodes, that serve as position references. In this paper, we show that sensors acquire coarse-grain location awareness by the training protocol. The training protocol which hybrids the synchronization and training procedure. In this protocol, synchronization and training are combined into one scheme. The sink node sends two beacons in each slot instead of one. In the training, sensor searching for its location using a binary search scheme. Our simulation results shown less number of cycles needed for training. [ABSTRACT FROM AUTHOR]