Showing papers in "International Journal of Distributed Sensor Networks in 2016"
TL;DR: This paper proposes a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors and concludes that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.
Abstract: With the application of Internet of Things and services to manufacturing, the fourth stage of industrialization, referred to as Industrie 4.0, is believed to be approaching. For Industrie 4.0 to come true, it is essential to implement the horizontal integration of inter-corporation value network, the end-to-end integration of engineering value chain, and the vertical integration of factory inside. In this paper, we focus on the vertical integration to implement flexible and reconfigurable smart factory. We first propose a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors. Then, we elaborate the operational mechanism from the perspective of control engineering, that is, the smart artifacts form a self-organized system which is assisted with the feedback and coordination blocks that are implemented on the cloud and based on the big data analytics. In addition, we outline the main technical features and beneficial outcomes and present a detailed design scheme. We conclude that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.
1,108 citations
TL;DR: An overview of the applications of activity recognition systems is provided and a comparison of the existing methodologies which, when applied to real-world scenarios, allow to formulate research questions for future approaches are compared.
Abstract: Activity recognition systems are a large field of research and development, currently with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on...
172 citations
TL;DR: The evolution from intelligent vehicle grid to autonomous, Internet-connected vehicles, and vehicular fog is discussed, the equivalent of instantaneous Internet cloud for vehicles, providing all the services required by the autonomous vehicles.
Abstract: Recent advances in communications, controls, and embedded systems have changed the perception of a car. A vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. It is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control, and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable of making its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g. the smart building), the Internet of Vehicles will have communications, storage , intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the vehicular fog, the equivalent of instantaneous Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from intelligent vehicle grid to autonomous, Internet-connected vehicles, and vehicular fog.
148 citations
TL;DR: A traffic anomaly detection algorithm for wireless sensor networks (WSNs) which considers the particular imbalanced, nonstationary properties of the WSN traffic and the limited energy and computing capacity of the wireless sensors at the same time.
Abstract: Traffic anomaly detection is emerging as a necessary component as wireless networks gain popularity. In this paper, based on the improved Autoregressive Integrated Moving Average (ARIMA) model, we propose a traffic anomaly detection algorithm for wireless sensor networks (WSNs) which considers the particular imbalanced, nonstationary properties of the WSN traffic and the limited energy and computing capacity of the wireless sensors at the same time. We systematically analyze the characteristics of WSN traffic, the causes of WSN abnormal traffic, and the latest related research and development. Specifically, we improve the traditional time series ARIMA model to make traffic prediction and judge the traffic anomaly in a WSN. Simulated and real WSN traffic data gathered from University of North Carolina are used to carry out simulations on Matlab. Simulation results and comparative analyses demonstrate that our proposed WSN traffic anomaly detection scheme has better anomaly detection accuracy than traditional traffic anomaly detection algorithms.
91 citations
TL;DR: In this article, a wireless sensor network (WSN) has increased tremendously throughout the years, and sensor nodes are deployed to operate autonomously in remote environments in WSNs.
Abstract: Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network...
63 citations
TL;DR: A two-layer HMM is proposed to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data.
Abstract: Activities of Daily Livings (ADLs) refer to the activities that are carried out by an individual for everyday living. Recognition of ADLs is key element for building intelligent and pervasive environments. We propose a two-layer HMM to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data. We used embedded sensor with appliances or object to get object used sequence data as well as object name, type, interaction time, and location. In the first layer, we use location data of object used sensor to predict the activity class and in the second layer object used sequence data to determine the exact activity. We perform comparison with other activity recognition models using three real datasets to validate the proposed model. The results show that the proposed model achieves significantly better recognition performance than other models.
57 citations
TL;DR: In the paper, the reference architecture of SELFNET, which is divided into Infrastructure Layer, Virtualized Network Layer, SON Control Layer,SON Autonomic Layer, NFV Orchestration and Management Layer, and Access Layer, will be presented.
Abstract: To meet the challenging key performance indicators of the fifth generation (5G) system, the network infrastructure becomes more heterogeneous and complex. This will bring a high pressure on the reduction of OPEX and the improvement of the user experience. Hence, shifting today's manual and semi-automatic network management into an autonomic and intelligent framework will play a vital role in the upcoming 5G system. Based on the cutting-edge technologies, such as Software-Defined Networking and Network Function Virtualization, a novel management framework upon the software-defined and Virtualized Network is proposed by EU H2020 SELFNET project. In the paper, the reference architecture of SELFNET, which is divided into Infrastructure Layer, Virtualized Network Layer, SON Control Layer, SON Autonomic Layer, NFV Orchestration and Management Layer, and Access Layer, will be presented.
57 citations
TL;DR: A new weighted evidence combination on the basis of the distance between evidence and entropy function is presented and can efficiently cope with high conflicting evidences with better performance of convergence.
Abstract: Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and...
56 citations
TL;DR: A vehicular cloud architecture to assist in the management of large cities is proposed that will create a framework to support different types of services as well as provide storage mechanisms, access, and information management which includes tools for different modes of transport.
Abstract: An intelligent transport system (ITS) is intended to streamline the operations of vehicles, manage vehicle traffic, and help drivers with safety and other information, as well as supply convenient applications for passengers. This system is essential for tackling the problems of a big city, like traffic congestion and a lack of a communication infrastructure or traffic engineering, among other factors. With these challenges in mind, we propose a vehicular cloud architecture to assist in the management of large cities. This will create a framework to support different types of services as well as provide storage mechanisms, access, and information management which includes tools for different modes of transport not only for citizens but also for commercial vehicles and emergency services like ambulances. In addition, it will be possible to increase the capacity for abstraction to meet information needs through the use of vehicular networks and the integration of VANETs with other networks, so as to provide...
54 citations
TL;DR: A dedicated short-range communication/long-term evolution/WiFi-based vehicular system is developed to support the vehicle-to-vehicle and vehicle- to-pedestrian communication for the safety of vehicles and pedestrians and experiment results reveal that IEEE 802.11p based vehicle-To-Vehicle communication is unstable in the non-line-of-sight conditions.
Abstract: The dedicated short-range communication/wireless access for vehicular environment together with the fourth generation-long-term evolution technologies has been widely accepted as the most promising...
53 citations
TL;DR: Both the theoretical analysis and experimental results show that verifiable privacy-preserving range query is capable of protecting the privacy sensor data, query result, and query range, which also supports the completeness verification of the query result.
Abstract: In the field of wireless sensor networks, the secure range query technique is a challenging issue. In two-tiered wireless sensor networks, a verifiable privacy-preserving range query processing method is proposed that is based on bucket partition, information identity authentication, and check-code fusion. During the data collection process, each sensor node puts its collected data into buckets according to the bucket partition strategy, encrypts the non-empty buckets, generates the check-codes for the empty buckets, and fuses them. Then, the check-codes and the encrypted buckets are submitted to the parent node until they reach the storage node. During query processing, the base station converts the queried range into the interested bucket tag set and sends it to the storage node. The storage node determines the candidate-encrypted buckets, generates the check-code through code fusion, and sends them to the base station. The base station obtains query results and verifies the completeness of the result w...
TL;DR: A person-following shopping cart assistance robot, capable of helping elderly people to carry products in a supermarket and connected to a portable device like a smartphone or tablet, thus providing ease of use to the end user.
Abstract: Technology has recently been developed which offers an excellent opportunity to design systems with the ability to help people in their own houses. In particular, assisting elderly people in their environments is something that can significantly improve their quality of life. However, helping elderly people outside their usual environment is also necessary, to help them to carry out daily tasks like shopping. In this paper we present a person-following shopping cart assistance robot, capable of helping elderly people to carry products in a supermarket. First of all, the paper presents a survey of related systems that perform this task, using different approaches, such as attachable modules and computer vision. After that, the paper describes in detail the proposed system and its main features. The cart uses ultrasonic sensors and radio signals to provide a simple and effective person localization and following method. Moreover, the cart can be connected to a portable device like a smartphone or tablet, thus providing ease of use to the end user. The prototype has been tested in a grocery store, while simulations have been done to analyse its scalability in larger spaces where multiple robots could coexist.
TL;DR: An indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN) to reduce the positioning error and shorten the prediction time is proposed.
Abstract: Wi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service LBS and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of indoor environment and the time delay caused by the time consumption to provide the position. In this paper, we propose an indoor localization system using the affinity propagation AP clustering algorithm and the particle swarm optimization based artificial neural network PSO-ANN. The clustering technique is adopted to reduce the maximum location error and enhance the prediction performance of PSO-ANN model. And the strong learning ability of PSO-ANN model enables the proposed system to adapt to the complicated indoor environment. Meanwhile, the fast learning and prediction speed of the PSO-ANN would greatly reduce the time consumption. Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.
TL;DR: The activity model based on 3D acceleration and gyroscope is created and the system identifies simulated falls from ADLs with a high accuracy of 97.7%, while sensitivity and specificity are 94% and 99%, respectively.
Abstract: The activity model based on 3D acceleration and gyroscope is created in this paper, and the difference between the activities of daily living (ADLs) and falls is analyzed at first. Meanwhile, the kNN algorithm and sliding window are introduced to develop a smart device enabled system for fall detection and alert, which is composed of a wearable motion sensor board and a smart phone. The motion sensor board integrated with triaxial accelerometer, gyroscope, and Bluetooth is attached to a custom vest worn by the elderly to capture the reluctant acceleration and angular velocity of ADLs in real time. The stream data via Bluetooth is then sent to a smart phone, which runs a program based on the kNN algorithm and sliding window to analyze the stream data and detect falls in the background. At last, the experiment shows that the system identifies simulated falls from ADLs with a high accuracy of 97.7%, while sensitivity and specificity are 94% and 99%, respectively. Besides, the smart phone can issue an alarm and notify caregivers to provide timely and accurate help for the elderly, as soon as a fall is detected.
TL;DR: A combinatorial auction system that determines winners at each bidding round according to the job's urgency based on execution time deadline in order to efficiently allocate resources and reduce the penalty cost.
Abstract: Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS Quality of Service constraints are satisfied and provider’s profit is maximized. In order to increase the profit, the penalty cost for SLA Service Level Agreement violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job’s urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider’s profit and success rate of job completion with conventional mechanism using real workload data.
TL;DR: This paper identifies the motivation and challenges for applying geographic routing in DTNs with the state of the art, and highlights the future research directions for this branch.
Abstract: Delay/Disruption Tolerant Networks (DTNs) have been attracting great interest from research community, where data communication naturally does not require contemporaneous end-to-end connectivity. Although they are suffering from a large variation of network topology, numerous previous routing protocols proposed for DTNs still make effort to qualify delivery potential, via network topology information. Geographic routing is an alternative, conceptually, by relying on the geographic information instead of topological information. In the literature, since this technique branch has not been extensively investigated in DTNs, our paper identifies the motivation and challenges for applying geographic routing in DTNs with the state of the art. Also, we highlight the future research directions for this branch.
TL;DR: This work presents a reputation model which builds both service reputation and feedback reputation, and proposes hidden-zone strategy and k-anonymity strategy to defend the reputation link attack during pseudonym changes.
Abstract: Establishing trust and reputation for evaluation of message reliability is key to the vehicular ad hoc networks (VANETs). Most of the previous reputation management systems focus on the effectiveness of the reputation management system in handling the liars who send false service messages. However, these reputation management systems have two drawbacks. One is that they are vulnerable to tactical attacks such as self-promoting attacks and bad-mouthing attacks. The other is that they may violate location privacy because they assume every vehicle communicates with a unique ID. Our research particularly investigates the robustness against these tactical attacks, as well as the preservation of privacy by integrating trust management with the pseudonym technique. To resist the tactical attacks in VANETs, we present a reputation model which builds both service reputation and feedback reputation. Moreover, we apply the information entropy and the majority rule to the reputation accumulation algorithms to counter false feedback. To defend the reputation link attack during pseudonym changes, we propose hidden-zone strategy and -anonymity strategy. The simulation results show that our scheme is robust to these tactical attacks and preserves privacy against the reputation link attack during the pseudonym changes.
TL;DR: This paper presents a fingerprinting based localization technique using deep belief network (DBN) and ultrawideband (UWB) signals in an office environment and shows that, with appropriate fingerprinting database and model structure, the location system can get desired accuracy.
Abstract: With the increasing requirement of localization services in indoor environment, indoor localization techniques have drawn a lot of attention. In recent years, fingerprinting localization techniques have been proved to be effective in indoor localization tasks. Due to the complexity and variability of indoor environment, some traditional geometric localization techniques based on time of arrival (TOA), received signal strength (RSS), or direction of arrival (DOA) may cause big position errors. Unlike common geometric localization methods, fingerprinting localization techniques estimate the position of target by creating a pattern matching model or regression model for the measurement. Therefore, a suitable learning model is the key of a fingerprinting location system. This paper presents a fingerprinting based localization technique using deep belief network (DBN) and ultrawideband (UWB) signals in an office environment. Some location-dependent parameters extracted from channel impulse response (CIR) are used as signatures to build the fingerprinting database. The construction of DBN which is based on the fingerprinting database is also discussed in this paper. Experiment results show that, with appropriate fingerprinting database and model structure, the location system can get desired accuracy.
TL;DR: An intelligent strategy is proposed that allows UAVs to perform tactical movements in a disaster scenario, combining the Jaccard distance and artificial intelligence algorithms like hill climbing and simulated annealing to maximizes the number of victims that are serviced by the Uavs while avoiding network disconnections.
Abstract: Unmanned Aerial Vehicles UAVs are envisioned as flexible and fast-deploying communication network for disaster scenarios, where the typical communication infrastructure is likely to be malfunctioning. A few works propose UAVs for building communication links autonomously between rescue team’s members in disaster scenarios. The techniques used are usually based on navigation, positioning, and signal strength processing. However, these techniques may not be enough if the objective is to provide communication services to the maximum number of victims and rescuers and not only to a few rescuers. In this situation, dissimilarity metrics, like the Jaccard distance, can provide information about whether the communication service provided to victims is efficient or not e.g., providing a better distribution of the victims assigned to each UAV acting as service provider. We propose an intelligent strategy that allows UAVs to perform tactical movements in a disaster scenario, combining the Jaccard distance and artificial intelligence algorithms like hill climbing and simulated annealing. Our strategy maximizes the number of victims that are serviced by the UAVs while avoiding network disconnections. Also, a mobility model specifically developed for modelling the victims’ movements within the incident site of a disaster scenario is proposed.
TL;DR: This work provides description of data structures and practical implementation principles of the proposed structures transmitted by SIP as a promising enabler for efficient M2M communication in the IoT world.
Abstract: Internet of Things IoT is expected to become a driver in an emerging era of interconnected world through the advanced connectivity of smart devices, systems, and services. IoT goes beyond a broad range of Machine-to-Machine M2M communication technologies and covers a wide variety of networking protocols. There exist solutions like MQTT or SIP collecting data from sensors, CoAP for constrained devices and networks, or XMPP for interconnecting devices and people. Also there is a plethora of standards and frameworks OSGi, AllJoyn bringing closer the paradigm of IoT vision. However, the main constraint of most existing platforms is their limited mutual interoperability. To this end, we provide a comprehensive description of protocols suitable to support the IoT vision. Further, we advocate an alternative approach to already known principles and employ the SIP protocol as a container for M2M data. We provide description of data structures and practical implementation principles of the proposed structures JSON and Protocol Buffers are discussed in detail transmitted by SIP as a promising enabler for efficient M2M communication in the IoT world. Our reported findings are based on extensive hands-on experience collected after the development of advanced M2M smart home gateway in cooperation with the operator Telekom Austria Group.
TL;DR: 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: 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.
TL;DR: A simple analytical model able to predict the energy efficiency and reliability of different data gathering techniques is derived and could be a useful tool in the problem of data gathering in wireless sensor networks.
Abstract: We study the problem of data gathering in wireless sensor networks and compare several approaches belonging to different research fields; in particular, signal processing, compressive sensing, information theory, and networking related data gathering techniques are investigated. Specifically, we derived a simple analytical model able to predict the energy efficiency and reliability of different data gathering techniques. Moreover, we carry out simulations to validate our model and to compare the effectiveness of the above schemes by systematically sampling the parameter space i.e., number of nodes, transmission range, and sparsity. Our simulation and analytical results show that there is no best data gathering technique for all possible applications and that the trade-off between energy consumptions and reliability could drive the choice of the data gathering technique to be used. In this context, our model could be a useful tool.
TL;DR: The state-of-the-art technology in the area of Machine-to-Machine (M2M) communication is reviewed by comparing the M2M concept with other related research paradigms such as Wireless Sensor Networks, Cyber-Physical Systems, Internet of Things, and Human-Agent Collectives.
Abstract: Communication is a prerequisite for any form of social activity, including social networking. Nowadays, communication is not reserved only for humans, but machines can also communicate. This paper reviews the state-of-the-art technology in the area of Machine-to-Machine (M2M) communication by comparing the M2M concept with other related research paradigms such as Wireless Sensor Networks, Cyber-Physical Systems, Internet of Things, and Human-Agent Collectives. Furthermore, the paper analyses trends in the interconnecting of machines and identifies an evolutionary path in which future (smart) machines will form mostly or completely autonomous communities bonded through social connections. Such communities—machine social networks—will be formed dynamically, just like human connections, and based on the needs of machines, their context, and state of their environment. Finally, the paper outlines the current evolutionary stage and identifies key research challenges of machine social networking.
TL;DR: The results show that Mk-means (modified k-me means) algorithm was found to outperform the existing clustering algorithms owing to its unique multiple cluster head methodology.
Abstract: A major problem with Wireless Sensor Networks WSNs is the maximization of effective network lifetime through minimization of energy usage in the network nodes. A modified k-means Mk-means algorithm for clustering was proposed which includes three cluster heads simultaneously chosen for each cluster. These cluster heads CHs use a load sharing mechanism to rotate as the active cluster head, which conserves residual energy of the nodes, thereby extending network lifetime. Moreover, it reduces the number of times reclustering has to be done and significantly increases the number of data packets sent during network operation. The results show that Mk-means modified k-means algorithm was found to outperform the existing clustering algorithms owing to its unique multiple cluster head methodology.
TL;DR: The proposed road perception based geographical routing protocol named RPGR for VANET incorporates relative distance, direction, and midrange forwarder node with traffic density to forward the data toward destination in order to improve geographical forwarding between and at the intersections.
Abstract: Vehicular ad hoc networks (VANETs) are going to be an emerging multihop communication exploit among vehicles to deliver data packets. The special characteristics of vehicular network make the communication link between vehicles unreliable. To handle high mobility and environmental obstacles, most of geographical routing protocols do not consider stable links during packet transmission which lead to higher delay and packet dropping in network. In this paper, we propose road perception based geographical routing protocol named RPGR for VANET. The proposed routing protocol incorporates relative distance, direction, and midrange forwarder node with traffic density to forward the data toward destination in order to improve geographical forwarding between and at the intersections. Simulation results show that the proposed routing protocol performs better as compared to existing solutions.
TL;DR: The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem based on first-order optimal condition to improve the efficiency of search and has better localization accuracy compared to other range-based localization methods.
Abstract: Node localization is an important supporting technology in wireless sensor networks WSNs. Traditional maximum likelihood estimation based localization methods MLE assume that measurement errors are independent of the distance between the anchor node and a target node. However, such an assumption may not reflect the physical characteristics of existing measurement techniques, such as the widely used received signal strength indicator. To address this issue, we propose a distance-based MLE that considers measurement errors that depend on distance values in this paper. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution is developed based on first-order optimal condition to improve the efficiency of search. In addition, a two-dimensional search method is also presented. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared to other range-based localization methods.
TL;DR: Experimental results show that the proposed algorithm is very promising, which can improve the energy efficiency and the quality of data transmission in the sensor network significantly.
Abstract: Energy saving in wireless sensor networks is a fundamental issue as most sensor nodes are powered by batteries. The deployment of mobile sinks can alleviate the imbalance of energy consumption among sensor nodes, thereby prolonging the network lifetime. In this paper, we study the energy management problem in sensor networks, using multiple mobile sinks. We first formulate the problem as a novel data collection problem and then propose an efficient algorithm for it. The key challenge in the design of the proposed algorithm is how to balance the workload among mobile sinks and the energy consumption among sensor nodes through the control of the movement of mobile sinks. We finally evaluate the performance of the proposed algorithm through experimental simulation. Experimental results show that the proposed algorithm is very promising, which can improve the energy efficiency and the quality of data transmission in the sensor network significantly.
TL;DR: This special issue introduces state-of-the-art trends in the design and applications of UASNs, with a thorough analysis, algorithmic design, numerical simulations, and results from sea experiments presented.
Abstract: Ocean exploration, through the development of oceanobservation systems, has been recognized as a key step toward a fuller understanding of life on Earth. With the rapid developments in technology, underwater acoustic sensor networks (UASNs) will help to fulfill the needs of these ocean-observation systems, whose applications include gathering of scientific data, early warning systems, ecosystem monitoring, navigation aids, and military surveillance. The applications depending on UASNs are remote control in offshore oil industry, pollution monitoring in environmental systems, collection of scientific data recorded at oceanbottom stations, disaster detection and early warning, national security and defense (intrusion detection and underwater surveillance), as well as for the discovery of new resources. Due to its importance, UASN has played a key enabling technique for marine technology. Although a UASN shares many features of a traditional sensor network on land, it has many unique features which require special designs and techniques to handle them. The characteristics of the underwater acoustic channel introduce a unique design complexity into almost every layer of the network protocol stack. This includes low communication bandwidth which reflects on the transmission rate and requires packet sparsing, long propagation delay which necessitates the design of specialized scheduling mechanisms, high error probability that leads to need for two efficient transport protocols, and sensor node mobility which affects packet routing. This special issue introduces state-of-the-art trends in the design and applications of UASNs. Both theoretical and practical issues related to UASNs are considered. The focus is on the physical layer, the medium access control (MAC) layer, and the network layer. Out of the many high-quality submissions, the papers chosen focus on signal detection, channel coding, transmission scheduling, power control, and routing. The contributions in this special issue present a thorough analysis, algorithmic design, numerical simulations, and results from sea experiments. The original works in physical layer include three contributions. In UASNs, multipath propagation is unavoidable. The multipath propagation decreases the transmission efficiency and distorts the source signal. In ‘‘Alternative Approach for Combination of Fingers in Underwater Acoustic Communication,’’ the authors propose a more reliable rake receiver based on bit error rate (BER) of training sequence duration. The authors conducted experiments using simulation and also lake trials to evaluate its performance. The authors show that the uncoded BER of the proposed rake receiver is lower than that of the conventional rake receiver and that of the nonrake receiver. The proposed method also shows better performance with correct path detection using BER of training sequence duration. The authors of ‘‘The Partial Power Control Algorithm of Underwater Acoustic Sensor Networks Based on Outage Probability Minimization’’ focus on reducing the energy consumption of the UASN. A solution is offered to reduce interference due to highpower transmission by modeling the channel as an auto-regression process and estimating the transmission loss, thereby minimizing the outage probability in the network. In the work titled ‘‘Optimization of LDPC Codes over the Underwater Acoustic Channel,’’ the authors propose a channel coding scheme that combats the large delay spread in the channel through feedback from the channel equalizer and the channel decoder. The result is a low-density parity check decoder optimized to the unique channel conditions of the underwater acoustic channel. Due to the non-negligible physical restrictions of the UASN communication, most MAC protocols used in existing terrestrial sensor networks become inapplicable. The MAC layer of the network is considered in ‘‘MHM: A Multiple Handshaking MAC Protocol for Underwater Acoustic Sensor Networks.’’ The main idea is to allow multiple nodes to transmit and receive data packets at the same time. The authors propose a multiple handshaking MAC protocol for three UASNs. Using the multiple handshaking and a competitive mechanism of control packets, the new protocol makes the contending nodes share the underwater acoustic channel much more fairly and efficiently. Analysis and simulation results are used to validate their claims. The authors of ‘‘Throughput and Delay Analysis of an Underwater CSMA/CA Protocol with MultiRTS and Multi-DATA Receptions’’ proposed an
TL;DR: The proposed scheme is the first attempt for resolving handover of net-drones in the three-dimensional space and uses the seamless handover success probability and the false handover initiation probability in order to evaluate the optimal coverage decision algorithm.
Abstract: The advent of the Internet of things IoT is changing the way how we interact with the physical world. However, the current Internet suffers from exponential increase in bandwidth demand. In order to resolve the bandwidth issue, we can consider aerial networks by unmanned aerial vehicles UAV or the so-called drones for establishing a three-dimensional mobile network in an ad hoc manner. By deploying a network from the sky, we can use the otherwise idle wireless medium and high mobility free from ground obstacles. Aerial networks are especially effective for supporting the temporary surge of population as well as disaster areas because building an additional network infrastructure requires extensive time. In this paper, we propose an efficient handover mechanism for aerial networks in the three-dimensional space, which significantly differs from the conventional two-dimensional schemes. The proposed scheme adjusts the height of a drone and the distance between the drones. To this end, we use the seamless handover success probability and the false handover initiation probability in order to evaluate the optimal coverage decision algorithm. To the best of our knowledge, the proposed scheme is the first attempt for resolving handover of net-drones in the three-dimensional space.
TL;DR: This work puts forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing, and performs significantly better than other ensemble approaches.
Abstract: Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drift can be categorized into two different approaches: online and block-based approaches. The primary disadvantage of the block-based ensembles lies in the difficulty of tuning the block size to provide a tradeoff between fast reactions to drifts. Motivated by this challenge, we put forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing. The algorithm uses the adaptive windowing as a change detector. Once a change is detected, a new classifier is built replacing the worst one in the ensemble. By experimental evaluations on both synthetic and real-world datasets, our method performs significantly better than other ensemble approaches.