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This paper describes the underwater data collection utilizing stationary sensor node and movable sensor node. We consider applying the system to investigate the conditions of marine ecosystem around the coral reef at Okinawa, Japan. The framework and fundamental design of our proposed underwater information gathering system under development are introduced. It is also reported the development of the prototypes of the sensory node and movable sensor node. Basic examinations utilizing such prototype at underwater environment are also described. This paper also describes experimental results of image gathering from via wireless local area network communication. Our future trials based on the current developmental situation are also discussed. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) refers to a topology of miniaturized embedded systems in and on a human body using wireless communication technology. Using the WBAN, human body data is collected to be used in various areas, including basic health information collection management, improvement of athletics capabilities, and systematic management of patients. To make use of the system in such areas efficiently, a fault or abnormal symptoms monitoring system for the sensors formed in the human body is required. Since data collection from a specific athlete can be taken care of by many people, malfunction or failure of sensors can be immediately treated. However, for patients with a variety of diseases, concentrating on their individual health information takes much effort and is time consuming. Further, when one patient is taken care of, other patients might be neglected. The present paper proposes a visual monitoring system for fault tolerance (VMSFT) that can monitor patients in a WBAN while also noticing the failure of sensors immediately. VMSFT can monitor persons who form a WBAN and infer a sensor's failure through the collected data. If VMSFT is interlinked with the emergency room, it is possible to take care of the patients proactively. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) require optimized energy consumption and an improved packet delivery ratio (PDR) for optimal performance. Clustering and routing strategies are frequently used to reduce energy usage, while multiradio (MR) multichannel (MC) systems improve PDR levels. Transmission power control (TPC) features in both energy consumption reduction and PDR improvement. While the mentioned techniques are used in previous studies, they were investigated separately and without a holistic approach. This study discusses the challenge of obtaining high‐PDR, energy‐efficient, and cost‐effective clustering and routing in WSNs. As a means of reducing deployment costs, a heterogeneous setting with energy‐constraint normal sensors for environmental monitoring as well as some high‐energy, TPC‐enabled, MR super nodes is assumed. The super nodes are considered as CHs and are responsible for collecting the sensed data from the normal sensors. Installing additional radios on super nodes enables static channel assignment (CA), which yields high PDR with low imposed overhead on the network. The mentioned TPC‐based MC heterogeneous setting, which yields cost‐effectiveness, high PDR, and energy efficiency, was not investigated in previous studies. The considered problem is decoupled into two phases of configuring the super nodes and normal sensors, which are solved using the grey wolf optimization (GWO) algorithm. The performed simulations show an average improvement of our proposed algorithm in PDR, energy consumption, consumed energy per delivered bit, and network lifetime by 9%, 30%, 65%, and 44%, respectively. [ABSTRACT FROM AUTHOR]

In multisink wireless sensor networks, synchronized data collection among multiple sinks is a significant and challenging task. In this paper, we propose an unbalanced threshold based distributed data collection scheme to reconstruct the synchronized sensed data of the whole sensor network in all sinks. The proposed scheme includes the unbalanced threshold based distributed top-K query algorithm and the distributed iterative hard thresholding algorithm. By computing unbalanced thresholds and pruning unnecessary element exchanging, each sink can synchronize the top-K aggregated values efficiently via the unbalanced threshold based distributed top-K query algorithm. After that, the synchronized sensed data of the whole sensor network can be reconstructed through the distributed iterative hard thresholding algorithm in a distributed and cooperative manner. We show through experiments that the proposed scheme can reduce the interaction times and decrease the number of transmitted data and that of computed data compared to the existing schemes while maintaining the similar data reconstruction accuracy. The communication and computational performances of the proposed scheme are also analyzed in detail in the paper. [ABSTRACT FROM AUTHOR]

We present our experience with design, development, deployment, and operation of a nation-wide network of mobile Internet measurement probes. Mobile internet is becoming increasingly important, with new technologies being deployed on a regular basis. In the last decade we have seen a transition over a multitude of data transmission technologies, from EDGE, UMTS, and HSPA to LTE, with 5G already on the horizon. This presents an extremely heterogeneous environment where problems are difficult to pinpoint, because of either the fast changing radio channel conditions, daily mobility of the user base, or a number of fallback technologies that are available at a specific location and chosen by the terminal itself. To quantify these conditions, we have developed a mobile internet measurement probe called QMON, which can be either statically deployed or used in drive measurements and is able to collect hundreds of key performance indicators on physical, network, and application layers of the network stack, acting at the same time as an event-driven real-time sensor network and a batch-mode detailed data collection device. In this paper we expose some of the design considerations and pitfalls; among them are the problems of managing and monitoring the remote probes that measure the same communication channel that is also used for their control. [ABSTRACT FROM AUTHOR]

The main focus of this paper is the resilience of communication protocols for data gathering in distributed, large scale, and dense networks. In our previous work, we have proposed the resilient methods based on random behavior and data replications to improve route diversification, thus to take advantage of redundant network structure. Following these previous methods, we propose in this paper a new resilient method based on network coding techniques to improve resilience in Wireless Sensor Networks (WSNs) for smart metering applications. More precisely, using our resilience metric based on a performance surface, we compare several variants of a well-known gradient based routing protocol with the previous methods (random routing and packet replications) and the new proposed methods (two network coding techniques). The proposed methods outperformed the previous methods in terms of data delivery success even in the presence of high attack intensity. [ABSTRACT FROM AUTHOR]

The new generation of multimedia surveillance systems integrates a large number of heterogeneous sensors to collect, process, and analyze multimedia data for identifying events of potential security threats. Some of the major concerns facing these systems are scalability, ubiquitous access to sensory data, event processing overhead, and massive storage requirements--all of which demand novel scalable approach. Cloud computing can provide a powerful and scalable infrastructure for large-scale storage, processing, and dissemination of sensor data. Furthermore, the integration of sensor technology and cloud computing offers new possibilities for efficient development and deployment of sensor-based systems. This paper proposes a framework for a cloud-based multimedia surveillance system and highlights several research and technical issues. A prototype surveillance system is also designed and analyzed in the context of the proposed surveillance framework. The paper finally reports that cloud-based multimedia surveillance system can effectively support the processing overload, storage requirements, ubiquitous access, security, and privacy in large-scale surveillance settings. [ABSTRACT FROM AUTHOR]

This paper presents RECPE, a reliable collection protocol for aggregating data packets from all the sensor nodes to the sink in a large-scale WSN (wireless sensor network). Unlike some well-known reliable data collection protocols such as CTP (Collection Tree Protocol) that uses ETX (expected transmission count) as the routing metric, RECPE exploits ETF (expected transmission count over forward links) to construct a one-way collection tree, which avoids missing some good routes and reduces the effect of asymmetric link in the network. Crucially, RECPE guarantees the reliability by erasure-resilient codes in the application layer without retransmission required by other reliable protocols. Therefore, some lower layers such as data link layer only need to conduct best-effort data delivery. Meanwhile, to improve efficiency, RECPE also exploits Trickle algorithm to reduce routing beacons and pipeline data delivery to prevent self-interference. We evaluate the performance of RECPE via TOSSIM simulations, and our results show that, in comparison with CTP (the de facto data collection protocol for TinyOS), RECPE can obtain significant performance in terms of delivery cost, latency, and packet loss rate for reliable data collection especially in asymmetric link networks. [ABSTRACT FROM AUTHOR]

Data collection is one of the most important operations in wireless sensor networks. Currently, many researches focus on using a connected dominating set to construct a virtual backbone for data collection in WSNs. Most researchers concentrate on how to construct a minimum connected dominating set because a small virtual backbone incurs less maintenance. Unfortunately, computing a minimum size CDS is NP-hard, and the minimum connected dominating sets may result in unbalanced energy consumption among nodes. In this paper, we investigate the problem of constructing an energy-balanced CDS to effectively preserve the energy of nodes in order to extend the network lifetime in data collection. An energy-balanced connected dominating set scheme named DGA-EBCDS is proposed, and each node in the network can effectively transmit its data to the sink through the virtual backbone. When constructing the virtual backbone in DGA-EBCDS, we prioritize selecting those nodes with higher energy and larger degree. This method makes the energy consumption among nodes more balanced. Furthermore, the routing decision in DGA-EBCDS considers both the path length and the remaining energy of nodes in the path; it further prolongs the lifetime of nodes in the backbone. Our conclusions are verified by extensive simulation results. [ABSTRACT FROM AUTHOR]

Spatially organized clusters are basic structure for large-scale wireless sensor networks. A cluster is generally composed by a large amount of energy-limited low-tier nodes (LNs), which are managed by a powerful cluster head (CH). The low-tier nodes that are close to the cluster head generally become bottlenecks in data collection applications. Energy efficient scheduling is important for the low-tier sensors to be longevous while guaranteeing reliable communication. In this paper, based on three aspects of performance considerations including network longevity, multihop communication reliability, and sensing system cost minimization, we propose a stair duty-cycle scheduling method for the low-tier sensors. It is designed to make the LNs in the same cluster sleep cooperatively for most of the time and wake up in assigned sequence for multihop communication. Stair scheduling cannot only improve the energy efficiency of the network but also guarantee high communication reliability and low transmission delay. Efficiency of the proposed stair scheduling is verified by analysis and intensive simulations. The results show that the performances of stair scheduling are much better than that of random scheduling algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the localization of a timing signal source based on the time of arrival (TOA) measurements that are collected from nearby sensors that are position known and synchronized to each other. Generally speaking, for such TOA-based source localization, the corresponding observation equations contain nonlinear relationship between measurements and unknown parameters, which normally results in the nonexistence of any efficient unbiased estimator that attains the Cramer-Rao lower bound (CRLB). In this paper, we devise a new approach that utilizes linearization and adopts suitable coordinate system translation to eliminate nonlinearity from the converted observation equations. The performance analysis and simulation study conducted show that our proposed algorithm can achieve the CRLB when the zero-mean Gaussian and independent measurement errors are sufficiently small. [ABSTRACT FROM AUTHOR]

Mobility-assistant sensor networks comprise mobile elements, and static sensors are established for the purpose of solving the serious problems such as overlapping or energy holes in wireless sensor networks (WSNs). In such systems, most of the energy is consumed when the radios are on, waiting for a mobile sink (MS) to arrive. Sleep/wake scheduling is an effective mechanism to prolong the lifetime of these energy-constrained wireless sensors. However, sleep/wake scheduling could result in substantial discovery delays because the sensor needs time to receive the beacon-ID signals when MS entered its communication range. In this paper, we first study on the MS discovery mechanism and the factors which affect the efficiency of data collection. Based on these results, we then provide a solution to the control problem of how to optimally adjust the system parameters of the sleep/wake scheduling protocol to maximize the network lifetime, subject to a constraint on the expected residual contact time. Our numerical results indicate that the proposed solution can balance the network consumption, especially in sparse sensor networks. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of pavement roughness measurement and reduce the cost, an integrated and wireless transfer based measuring system was developed. The proposed system can obtain vehicles status and location data via wireless acceleration sensors and GPS, calculate the international roughness index (IRI) by power spectral density analysis, and provide reports automatically. This paper presents the architecture of the proposed system, consisting of data collector, car mounted terminal, and information platform. Two wireless communication systems (ZigBee and 3G modules) were utilized to transfer the data and construct network between the components. The information platform implemented an acceleration-IRI model to calculate IRI, and a GPS based distance algorithm was employed to segment the measured road per 1 km. The various results are saved in an Oracle database, displayed on the digital map and made available to the mobile terminal. Several field tests of the prototype system were conducted in Huzhou, Zhejiang province in China. The results show that, compared to the laser roughness testing method, the relative error of this proposed system is less than 10%, which verifies the accuracy, effectiveness, and reliability of the proposed measuring system. [ABSTRACT FROM AUTHOR]

Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive sensing can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper, we investigate data gathering in wireless sensor networks using CS and aim at constructing a maximum-lifetime data-gathering tree. The lifetime of the network is defined as the number of data-gathering rounds until the first node depletes its energy. Based on the hybrid-CS data-gathering model, we first construct an arbitrary data-gathering tree and then use the random switching decision and optimal parent node selecting strategy to adjust the load of the bottleneck node and prolong the network lifetime. Simulation results show that the proposed algorithm outperforms several existing approaches in terms of network lifetime. [ABSTRACT FROM AUTHOR]

Several studies investigating data validity and security against malicious data injection attacks in vehicular ad hoc networks (VANETs) have focused on trust establishment based on cryptology. However, the current researching suffers from two problems: (P1) it is difficult to distinguish an authorized attacker from other participators; (P2) the large scale of the system and high mobility set up an obstacle in key distribution with a security-based approach. In this paper, we develop a data-centric trust mechanism based on traffic flow theory expanding the notion of trust from intrusion-rejecting to intrusion-tolerant. First, we use catastrophe theory to describe traffic flow according to noncontinuous, catastrophic characteristics. Next, we propose an intrusion-tolerant security algorithm to protect traffic flow data collection in VANETs from malicious data injection attacks, that is, IA2P, without any security codes or authentication. Finally, we simulate two kinds of malicious data injection attack scenarios and evaluate IA2P based on real traffic flow data from Zhongshan Road in Dalian, China, over 24 hours. Evaluation results show that our method can achieve a 94% recognition rate in the majority of cases. [ABSTRACT FROM AUTHOR]

In severe disaster environments, data-gathering wireless sensor networks possibly suffer from spatial node failure due to external factors such as land slide and river overflow. In order to enhance tolerance to spatial node failure, this paper proposes not only a multipath data gathering protocol called “Side Trip (ST)” scheme but also an improved version of ST scheme called “Side Trip with Network Coding (STNC)” scheme. In the ST scheme, a node establishes a secondary path which passes through nodes almost the same hop count away from the sink in order to spatially separate the path from the primary path and enhance spatial node-failure tolerance. In the STNC scheme, redundant packets from different source nodes are network-coded into an encoded packet in order to reduce the number of redundant packets and the impact on the system lifetime. We compare the ST scheme, STNC scheme, and two existing multipath protocols (i.e., H-SPREAD (Hybrid-Secure Protocol for REliable dAta Delivery) scheme and SMRP (Subbranch Multipath Routing Protocol) scheme) by means of a self-developed simulator. Simulation results show that STNC scheme and ST scheme enhance tolerance to node-failure compared to H-SPREAD scheme and SMRP scheme with no significant impact on system lifetime. [ABSTRACT FROM AUTHOR]

Data collection with high efficiency is a key problem for the completion of crowdsensing tasks in Mobile Crowdsensing Networks. Much work has been done on the opportunistic routing problem, but they usually just focused on the sociality of node, and the attributes of node itself have always been ignored, such as energy and buffer, which are particularly important for the persistent crowdsensing tasks. In this paper, we now modify the spectral clustering algorithm and propose a novel and more practical routing protocol for MCSN, named EBRP, in which the sociality of node and the dynamic changes of the attributes of node itself are all fully considered when the relay nodes are selected. To evaluate the performance of our routing protocol, we have done extensive simulations and much analysis based on three real-life mobility traces. The results show that our EBRP can greatly outperform the existing protocols in terms of delivery ratio, cost, and node survival ratio. [ABSTRACT FROM AUTHOR]

With the growing need for the remote caring at home and the everincreasing popularity of mobile devices, more and more applications are being developed to enable remote health monitoring. Combining cloud computing with mobile technologies has enabled the healthcare service provider to offer continuous health data collection and physicians to conveniently monitor and assess users’ health while they are at home. However, users’ health data contain highly confidential information, and servers in the cloud are out of users’ trusted domain. Therefore, users may be reluctant to take advantage of remote health monitoring systems before they make sure their data are properly protected. In this paper, we propose a secure continuous remote health monitoring system named SCRHM with the main component, a searchable encryption scheme supporting range searches for remote health monitoring system. With this scheme, remote health monitoring service provider is able to detect outliers over encrypted health parameters. Using analysis, we prove the correctness and security of the proposed scheme on privacy protection of users’ health data. Via simulation experiments, we validate the performance of the proposed scheme in terms of computation and communication overhead. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are deployed in large areas to monitor a number of events in an area of interest. Monitoring environmental events by distributed sensor networks faces the challenge of high power consumption requirement over time, due to the large number of packets required for multihop data collection. To overcome the scalability issue of large scale WSNs, a proof-of-concept implementation demonstrates that integrating a mobile robot (MR) system with a clustering system for ZigBee WSNs will significantly increase the lifetime of the system, by conserving energy that the sensor nodes otherwise would use for communication. In this paper, two energy-efficient systems have been proposed: clustering and MR systems. The former divides the ZigBee WSN into smaller regions, allocates a cluster-head for each region, and aggregates the collected data, whereas the latter collects the sensed data from cluster-heads. The effectiveness of the proposed system has been demonstrated via simulation and experimental studies and verified that, using a single robot for data collection, the lifetime of the network can be extended by 2.3 times in average. [ABSTRACT FROM AUTHOR]

Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user's behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles' dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic. [ABSTRACT FROM AUTHOR]

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data. [ABSTRACT FROM AUTHOR]

Health social networks (HSNs) have become an integral part of healthcare to augment the ability of people to communicate, collaborate, and share information in the healthcare domain despite obstacles of geography and time. Doctors disseminate relevant medical updates in these platforms and patients take into account opinions of strangers when making medical decisions. This paper introduces our efforts to develop a core platform called Distributed Platform for Health Profiles (DPHP) that enables individuals or groups to control their personal health profiles. DPHP stores user’s personal health profiles in a non-proprietary manner which will enable healthcare providers and pharmaceutical companies to reuse these profiles in parallel in order to maximize the effort where users benefit from each usage for their personal health profiles. DPHP also facilitates the selection of appropriate data aggregators and assessing their offered datasets in an autonomous way. Experimental results were described to demonstrate the proposed search model in DPHP. Multiple advantages might arise when healthcare providers utilize DPHP to collect data for various data analysis techniques in order to improve the clinical diagnosis and the efficiency measurement for some medications in treating certain diseases. [ABSTRACT FROM AUTHOR]

A sensor-rich mobile video represents a new type of videos acquired from modern smart phones. During video recording, it is also recorded various amounts of sensor data collected from embedded sensors. Unlike the conventional videos acquired from proprietary capturing devices, these videos allow enriched reconstruction of their surrounding environments, while enabling users to record them handily. In this paper, we examine the robustness of existing device orientation detection method, by analyzing the motion sensor samples that are publicly available from the sensor-rich mobile video hosting website, and discuss our observation results and potential problems when computing device orientation of georeferential mobile videos. [ABSTRACT FROM AUTHOR]

With the development of the LEO satellite communication technology, highly dependable wireless communication and sensor data collection using LEO satellites have been getting much attention for emergency, marine research, and forest fire disaster in the remote region. The satellite system is expected to have the following features: rapid production, low cost, and fast construction of the satellite network. In this paper, a QS-CDMA uplink access technique in the LEO satellite is presented and discussed, which is focused on the local clocks using GPS 1PPS timing signals and the Doppler compensation for terminal uplink. The spreading code with length of 1023, which is used for the uplink preamble, selects the shift-m-sequence that can greatly reduce the MAI and increase the number of simultaneous access users. A novel analysis method for the accuracy of clock synchronization and a novel method for the estimation of Doppler shift and propagation delay are presented. These methods are used to guide the specific hardware implementation of the QS-CDMA LEO satellite sensor data collection system. Through simulations and experiments, it results in that this system structure can drastically reduce the complexity in implementing the acquisition in the satellite and increase the adaptability of the satellite system in different environments. [ABSTRACT FROM AUTHOR]

Wireless networks have been widely used in Cyber-Physical System (CPS) for data transmission. A CPS usually contains lots of sensors, and these sensors generate massive data. To make use of these data, we have to collect them through wireless communication. Sensors in a certain CPS do not always adopt same wireless technology, and these sensors formed heterogeneous wireless networks. Communication between different types of wireless networks can only be achieved by utilizing wireless gateways. In this paper, we address the problem of gateway placement for satisfying the bandwidth-requirement of each node by using minimum gateways. This problem can be formulated as a variant of Minimum Geometric Disk Cover problem which has been proved NP-complete. In order to solve our problem, one heuristic gateway placement algorithm and one grid-based heuristic algorithm are proposed. The result of simulation demonstrates that the heuristic algorithm can offer a good solution with big probability. [ABSTRACT FROM AUTHOR]

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy. [ABSTRACT FROM AUTHOR]

With the development of semiconductors and the resulting effect on manufacturing costs, wireless interface platforms have become increasingly powerful and popular. This has resulted in widespread applications ranging from daily life activities to military services. In large-scale applications of wireless sensor networks such as military surveillance, there are two important issues that determine the success of network services. The first is sensing coverage, the data collection process from the target that directly affects network traffic. The second is network lifetime which is based on the optimization of energy consumption. Power optimization for mobile platforms can be classified into two categories: efficient power based on protocol and extended interface. In this paper, we suggest an energy model for wireless sensor networks that is based on the first issue; it can be called Coverage and Energy Strategy for wireless sensor networks (CESS). The scheme will attempt to achieve optimal coverage of the sensing area and energy balanced scheduling for all sensors. It can reduce redundancy of working sensor nodes by defining minimal number of active nodes in a sensing area. Thus the network lifetime will be maximized by reducing redundancy power consumption. [ABSTRACT FROM AUTHOR]

In the packet access of wireless sensor networks, a distributed access protocol is employed to avoid packet collision but it also causes delay. Therefore, real time data collection is difficult. In wireless communication for simultaneous multidata collection (WC-SDC), sensed data are projected onto the parameters of the wireless communication. The specific feature of the sensed data appears in received signals. Even if the transmitted signal from each sensor collides by simultaneous access, the mixture sensed can be separated by using the specific features. Therefore, the real time data collection is achieved. However, frequency mismatch causes the fluctuation of sensed data, which gives the adverse impact to data separation. In this paper, a data tracking method is used for the data separation in the WC-SDC. We clarify the accuracy of data separation and the impact from the frequency offset. We propose a method for coping with the frequency offset and the error tracking. From the numerical results, our proposed method accurately achieves data separation even under 7% frequency offset normalized by the minimum frequency resolution. [ABSTRACT FROM AUTHOR]

With the increased use of smart phones, Wireless Multimedia Sensor Networks (WMSNs) will have opportunities to deploy such devices in several contexts for data collection and processing. While smart phones come with richer resources and can do complex processing, their battery is still limited. Background subtraction (BS) and compression techniques are common data reduction schemes, which have been used for camera sensors to reduce energy consumption in WMSNs. In this paper, we investigate the performance of various BS algorithms and compression techniques in terms of computation and communication energy, time, and quality. We have picked five different BS algorithms and two compression techniques and implemented them in an Android platform. Considering the fact that these BS algorithms will be run within the context of WMSNs where the data is subject to packet losses and errors, we also investigated the performance in terms of packet loss ratio in the network under various packet sizes. The experiment results indicated that the most energy-efficient BS algorithm could also provide the best quality in terms of the foreground detected. The results also indicate that data reduction techniques including BS algorithms and compression techniques can provide significant energy savings in terms of transmission energy costs. [ABSTRACT FROM AUTHOR]

In-network data aggregation is a widely used method for collecting data efficiently in wireless sensor networks (WSNs). The authors focus on how to achieve high aggregation efficiency and prolonging networks’ lifetime. Firstly, this paper proposes an adaptive spanning tree algorithm (AST), which can adaptively build and adjust an aggregation spanning tree. Owing to the strategies of random waiting and alternative father nodes, AST can achieve a relatively balanced spanning tree and flexible tree adjustment. Then a redundant aggregation scheme (RAG) is illustrated. In RAG, interior nodes help to forward data for their sibling nodes and thus provide reliable data transmission for WSN. Finally, the simulations demonstrate that (1) AST can prolong the lifetime and (2) RAG makes a better trade-off between storage and aggregation ratio, comparing to other aggregation schemes. [ABSTRACT FROM AUTHOR]

Recharging sensor networks using Unmanned Aerial Vehicles (UAVs) provides a possible method for increasing network lifetime. In this paper, we evaluate that approach, determining how much of a benefit it provides and under what conditions. We base our simulations and field experiments on data collected from charging with our UAV-based wireless power transfer system, which has similar transfer ranges and efficiencies as other such systems. We determine that a UAV can increase the network lifetime up to 290% compared to no recharging, that the UAV should recharge 30% of the sensor node battery capacity at one time for the maximum benefit, and that the UAV should recharge the lowest powered node until the network reaches a size of approximately 306 nodes at which point it should recharge the sink. We also examine how the sensor network can aid this through sink selection. The policy varies as network size increases, with a static approach working well until 200 nodes, and then either a perimeter or heuristic approach works best. These results inform future use of UAVs in recharging and working with sensor networks. [ABSTRACT FROM AUTHOR]

In low-duty-cycle wireless sensor networks, wireless nodes usually have two states: active state and dormant state. The necessary condition for a successful wireless transmission is that both the sender and the receiver are awake. In this paper, we study the problem: How fast can raw data be collected from all source nodes to a sink in low-duty-cycle WSNs with general topology? Both the lower and upper tight bounds are given for this problem. We use TDMA scheduling on the same frequency channel and present centralized and distributed fast data collection algorithms to find an optimal solution in polynomial time when no interfering links happen. If interfering links happen, multichannel scheduling is introduced to eliminate them. We next propose a novel Receiver-based Channel and Time Scheduling (RCTS) algorithm to obtain the optimal solution. Based on real trace, extensive simulations are conducted and the results show that the proposed RCTS algorithm is significantly more efficient than the link schedule on one channel and achieves the lower bound. We also evaluate the proposed data collection algorithms and find that RCTS is time-efficient and suffices to eliminate most of the interference in both indoor and outdoor environment for moderate size networks. [ABSTRACT FROM AUTHOR]

Constructive interference (CI) is a synchronous transmission technique for multiple senders transmitting the same packet simultaneously in wireless sensor networks (WSNs). CI enables fast and reliable network flooding in order to reduce the scheduling overhead of MAC protocols, to achieve accurate time synchronization, to improve link quality of lossy links, and to realize efficient data collection. By achieving microsecond level time synchronization, Glossy realizes millisecond level CI-based flooding and 99% reliability. However, Glossy produces substantial unnecessary data forwarding, which significantly reduces the network lifetime. This is a very critical problem, especially in energy-limited large-scale wireless sensor networks for agriculture and forestry (WSN-AF) system. In this paper, we present an energy adaptive CI-based flooding protocol (EACIF) by exploiting CI in WSN-AF. EACIF proposes a distributed active nodes selection algorithm (ANSA) to reduce redundant transmissions, thereby significantly reducing energy consumption and flooding latency. We estimate the performance of EACIF both with real data traces and with uniformly distributed topology. Simulation results show that EACIF achieves almost the same packet reception ratio (PRR) as Glossy (e.g., 99%), while reducing 63.96% energy consumption. EACIF also reduces 25% flooding latency. When the packet interval is 30 seconds, EACIF achieves 0.11% duty cycle. [ABSTRACT FROM AUTHOR]

Opinion mining, which extracts meaningful opinion information from large amounts of social multimedia data, has recently arisen as a research area. In particular, opinion mining has been used to understand the true meaning and intent of social networking site users. It requires efficient techniques to collect a large amount of social multimedia data and extract meaningful information from them. Therefore, in this paper, we propose a method to extract sentiment information from various types of unstructured social media text data from social networks by using a parallel Hadoop Distributed File System (HDFS) to save social multimedia data and using MapReduce functions for sentiment analysis. The proposed method has stably performed data gathering and data loading and maintained stable load balancing of memory and CPU resources during data processing by the HDFS system. The proposed MapReduce functions have effectively performed sentiment analysis in the experiments. Finally, the sentiment analysis results of the proposed system are very close to those of manual processes. [ABSTRACT FROM AUTHOR]

Recent studies reveal that great benefit can be achieved by employing mobile collectors to gather data in wireless sensor networks. Since the mobile collector can traverse the transmission range of each sensor, the energy of nodes may be saved near maximally. However, for directly receiving data packet from every node, the length of mobile collector route should be very long. Hence it may significantly increase the data gathering latency. To solve this problem, several algorithms have been proposed. One of them called BRH-MDG found that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. But, the BRH-MDG algorithm did not carefully analyze and optimize the energy consumption of the entire network. In this paper, we propose a mathematical model for the energy consumption of the LNs and present a new algorithm called EEBRHM. The simulation results show that under the premise of bounded relay hop, compared with BRH-MDG, EEBRHM can prolong the networks lifetime by 730%. [ABSTRACT FROM AUTHOR]

This paper proposes a new Energy and Latency Aware MAC (ELA-MAC) protocol that provides high energy-efficiency and low latency for data collection applications in wireless sensor networks. In previous research, B-MAC+ avoids the receiver node’s preamble overhearing of B-MAC with short preamble packets including only the destination address and the preamble packet number. However, like B-MAC, B-MAC+ has the sender node’s preamble overhead and communication latency. XY-MAC tries to solve these problems with inserting an early ACK between preamble packets. However, with the independence of each node’s wake-up point, the receiver node’s idle listening duration of XY-MAC is always longer than that of B-MAC+. ELA-MAC spreads collection-request without using an early ACK and delivers collection-response using an early ACK with considering the traffic types such as broadcasting and convergecasting. In this way, energy consumption on both downlink and uplink can be reduced. Besides, the time for collecting data can be shortened on uplink. With the experimental parameter obtained by the energy consumption analysis, the experiment was performed on a real sensor network test bed with various topologies. We obtained the result that ELA-MAC improves the energy efficiency by 12% and 37% compared to B-MAC+ and XY-MAC, respectively, and data collection time by 28% compared to B-MAC+. [ABSTRACT FROM AUTHOR]

We propose a three-phase top-k query based distributed data collection scheme which is designed for clustered or multisink wireless sensor networks. The proposed scheme consists of a distributed iterative hard thresholding algorithm and a three-phase top-k query algorithm. In the distributed iterative hard thresholding algorithm, the cluster heads or sink nodes reconstruct the compressed data in a distributed and cooperative manner. Meanwhile, the top-k query operation in the above algorithm is realized by pruning unnecessary elements among cluster heads or sink nodes in the three-phase top-k query algorithm. Simulation results show that there is no obvious difference in the performance of data reconstruction between our proposed scheme and existing compressive sensing theory based data collection schemes. However, both the number of interactions and the amount of transmitted data among cluster heads or sink nodes can be effectively reduced in the proposed scheme. The performance of the proposed scheme is analyzed in detail in this paper to support the claims. [ABSTRACT FROM AUTHOR]

Robustness and energy efficiency are critical for sensor information system, in which an abundance of wireless sensor nodes collects useful data from the deployed field. The chain-based protocols (like PEGASIS (Lindsey and Raghavendra, 2002)) are elegant solutions where sensor node has high energy efficiency. Unfortunately, if one node in the chain is failed due to some reasons such as energy exhaust, then the information cannot be forwarded to the sink. To improve system robustness and balance the energy consumption, this paper proposes a robust and energy-efficient data gathering (REEDG) approach, which is an improvement over the chain-based and grid-based network structures, in sensor information collecting system. In REEDG, data gathering is executed by a data transmitting chain which is composed by a series of virtual grids. Each grid communicates only with its neighbor grid and takes turns transmitting the information to the base station. Furthermore, an adaptive scheduling scheme is proposed to trade off energy consumption on each node and data forwarding delay. Experimental results show that, when compared with state-of-the-art approaches, REEDG achieves network lifetime extension of at least 13% as measured in terms of 20% dead nodes and improves the data transmission ratio at lowest 24% as 20% nodes fail. [ABSTRACT FROM AUTHOR]

The communication among heterogeneous embedded devices could lead to correctness problems in M2M environment. Sometimes, it is not easy to classify the data because they may provide wrong or uncertain information. The data from these devices should be gathered in a safe, efficient, and right manner without the help of server or human intervention; even the low-level information from each device causes interoperability problems. This data gathering or data fusion process is very important because the data mapping result could be understood as totally different situation and hence cause different reaction, feedback, and controls. In this paper, we propose a hierarchical aggregation for uncertain sensor data using reinforcement learning to get correct and efficient data gathering result for reliable wireless sensor network. In our proposal, we add a new category for uncertain data and classify them through reinforcement learning using hierarchical subcategories. By adopting our proposed aggregation, false classification caused by uncertain data can be decreased and the correctness of data gathering can be enhanced. [ABSTRACT FROM AUTHOR]

The vehicular infrastructures or roadside units (RSUs) in vehicular delay tolerant networks (VDTNs) can be used as the gateways of the distributed sensor networks. The different classes of service (CoS) support are desired when more than one type of the sensed data are collected by the RSUs. In this paper, the CoS support traffic scheduling problem for the RSU in VDTNs is considered. By exploring the contact information between the vehicles and the RSU, the CoS traffic scheduling problem is formulated as a maximum weighted triple matching problem, where the traffic scheduling strategy is a timeslot-vehicle-traffic matched pair. A low network based method is proposed to optimally solve the maximum weighted triple matching problem. Both the offline version and the online version of the traffic scheduling algorithm are developed. Extensive simulations are conducted and the simulation results show the effectiveness and efficiency of the proposed low network based algorithms. [ABSTRACT FROM AUTHOR]

This paper presents RECPE, a reliable collection protocol for aggregating data packets from all the sensor nodes to the sink in a large-scale WSN (wireless sensor network). Unlike some well-known reliable data collection protocols such as CTP (Collection Tree Protocol) that uses ETX (expected transmission count) as the routing metric, RECPE exploits ETF (expected transmission count over forward links) to construct a one-way collection tree, which avoids missing some good routes and reduces the effect of asymmetric link in the network. Crucially, RECPE guarantees the reliability by erasure-resilient codes in the application layer without retransmission required by other reliable protocols. Therefore, some lower layers such as data link layer only need to conduct best-effort data delivery. Meanwhile, to improve efficiency, RECPE also exploits Trickle algorithm to reduce routing beacons and pipeline data delivery to prevent self-interference. We evaluate the performance of RECPE via TOSSIM simulations, and our results show that, in comparison with CTP (the de facto data collection protocol for TinyOS), RECPE can obtain significant performance in terms of delivery cost, latency, and packet loss rate for reliable data collection especially in asymmetric link networks. [ABSTRACT FROM AUTHOR]

This paper studies the feasibility of a real-time monitoring system to provide prompt analysis support of workers' behaviour on dam construction sites. The system makes use of wireless sensor tracking technology and a set of servers for the running of software, handling collected data, real-time site state visualization, and remote interaction communications. The low-level ZigBee-based tracking technology network hardware utilizing fingerprinting software technology was employed. The system was found to be able to cover a large field whilst retaining its simple and easily déployable features. The proposed system was successfully implemented on the Xiluodu arch dam construction site and ultimately helped overcome the original shortcomings in worker safety management and has advanced the means of administration and managements awareness and culture to new levels. The field tests also show that the received signal strength indicator (RSSI)-based localization algorithm implemented on ZigBee devices is reliable and is accurate enough at 3 to 5 meters. In conclusion, the system proved to be very useful for the management of field safety worker behaviour analyses in the case of such a high arch dam and also for similar hydraulic projects. [ABSTRACT FROM AUTHOR]

Accelerated growth of urban population in the world put incremental stresses on metropolitan cities. Smart city centric strategies are expected to comprise solutions to sustainable environment and urban life. Acting as an indispensable role in smart city, IoT (Internet of Things) connects the executive ability of the physical world and the intelligence of the computational world, aiming to enlarge the capabilities of things in real city and strengthen the practicality of functions in cyber world. One of the important application areas of IoT in cities is food industry. Municipality governors are withstanding all kinds of food safety issues and enduring the hardest time ever due to the lack of sufficient guidance and supervision. IoT systems help to monitor, analyze, and manage the real food industry in cities. In this paper, a smart sensor data collection strategy for IoT is proposed, which would improve the efficiency and accuracy of provenance with the minimized size of data set at the same time. We then present algorithms of tracing contamination source and back tracking potential infected food in the markets. Our strategy and algorithms are evaluated with a comprehensive evaluation case of this IoT system, which shows that this system performs well even with big data as well. [ABSTRACT FROM AUTHOR]

The important function of a sensor network is to collect and forward data to destination. It is very important to know about the location of collected data. This kind of information can be obtained using localization technique in wireless sensor networks (WSNs). Localization is a way to determine the location of sensor nodes. Localization of sensor nodes is an interesting research area, and many works have been done so far. It is highly desirable to design low-cost, scalable, and efficient localization mechanisms for WSNs. In this paper, we discuss sensor node architecture and its applications, different localization techniques, and few possible future research directions. [ABSTRACT FROM AUTHOR]

Data collection is one of the most important operations in wireless sensor networks. Currently, many researches focus on using a connected dominating set to construct a virtual backbone for data collection in WSNs. Most researchers concentrate on how to construct a minimum connected dominating set because a small virtual backbone incurs less maintenance. Unfortunately computing a minimum size CDS is NP-hard, and the minimum connected dominating sets may result in unbalanced energy consumption among nodes. In this paper, we investigate the problem of constructing an energy-balanced CDS to effectively preserve the energy of nodes in order to extend the network lifetime in data collection. An energy-balanced connected dominating set scheme named DGA-EBCDS is proposed, and each node in the network can effectively transmit its data to the sink through the virtual backbone. When constructing the virtual backbone in DGA-EBCDS, we prioritize selecting those nodes with higher energy and larger degree. This method makes the energy consumption among nodes more balanced. Furthermore, the routing decision in DGA-EBCDS considers both the path length and the remaining energy of nodes in the path; it further prolongs the lifetime of nodes in the backbone. Our conclusions are verified by extensive simulation results. [ABSTRACT FROM AUTHOR]

Mobility-assistant sensor networks comprise mobile elements, and static sensors are established for the purpose of solving the serious problems such as overlapping or energy holes in wireless sensor networks (WSNs). In such systems, most of the energy is consumed when the radios are on, waiting for a mobile sink (MS) to arrive. Sleep/wake scheduling is an effective mechanism to prolong the lifetime of these energy-constrained wireless sensors. However, sleep/wake scheduling could result in substantial discovery delays because the sensor needs time to receive the beacon-ID signals when MS entered its communication range. In this paper, we first study on the MS discovery mechanism and the factors which affect the efficiency of data collection. Based on these results, we then provide a solution to the control problem of how to optimally adjust the system parameters of the sleep/wake scheduling protocol to maximize the network lifetime, subject to a constraint on the expected residual contact time. Our numerical results indicate that the proposed solution can balance the network consumption, especially in sparse sensor networks. [ABSTRACT FROM AUTHOR]

The aim of the study presented in this paper is to develop a flexible network structure for temperature monitoring of a super high arch dam under construction period. The multiple channel temperature acquisition method collects and analyzes system including flexible and stable field bus for the sensors, communication between intelligent module and control unit is proposed. In this temperature monitoring system, a total of 3 kinds of networks which independently marked by " (1), " (2), and " (3) were proposed, with " (1) being the lowest priority and " (3) being the highest priority. The lowest priority is field bus (ITU Bus) which connects all the grouped sensors into different channels of different intelligent acquisition modules. The ITU network protocol is a star type, and the network structure is independent on the sensors. The bus can connect different types of sensors such as strainmeter, stressmeter, jointmeter, rock-deformeter, piezometer et al., the measurement scope could be extended wider. This study provides a measuring technique has been successfully implemented in monitoring of the Xiluodu arch dam construction, and ultimately solved the shortcoming of manual measurement technique. [ABSTRACT FROM AUTHOR]