Abstract
With the rapid advent of the Internet of Things, sensor cloud, mobile Internet, and Web 3.0, more and more mobile devices, such as smart phones, Google glasses, and RFID, plus deployed various sensor networks, can sense and collect sensory data anytime and anywhere. We are moving toward the era of worldwide sensor networks, in which a huge amount of heterogeneous sensory data will be created every day and require advanced data management. In this setting, efficiently gathering, sharing, and integrating these spatial temporal data, and then deriving valuable knowledge timely, are a big challenge in this context. Furthermore, in the mobile environment, data management means a collection of centralized and distributed algorithms, architectures, and systems to store, process, and analyze the immense amount of spatial temporal data, where these data are cooperatively gathered through collections of mobile sensing devices which move in space over time. This special issue on mobile sensing and data management for sensor networks is intended to provide a forum for presenting, exchanging, and discussing the most recent advances in sensing and data management techniques.
To prolong the life time of each node in MSNs, energy model and conservation should be considered carefully when designing the data communication mechanism. The limited battery volume and high workload on channels worsen the life times of the busy nodes. In the paper “An probability-based energy model on cache coherence protocol with mobile sensor network,” the authors propose a new energy evaluating methodology of packet transmissions in MSNs, which is based on redividing network layers and describing the synchronous data flow with matrix form.
Mobile cloud computing (MCC) enables mobile devices to outsource their computing, storage, and other tasks onto the cloud to achieve more capacities and higher performance. In the paper “Adaptive computing resource allocation for mobile cloud computing,” the authors propose a novel MCC adaptive resource allocation model to achieve the optimal resource allocation in terms of the maximal overall system reward by considering both cloud and mobile devices. The adaptive resource allocation is modeled as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests.
Computation offloading is a popular approach for reducing energy consumption of mobile devices by offloading computation to remote servers. The paper “An energy-efficient multisite offloading algorithm for mobile devices” proposes an Energy-Efficient Multisite Offloading (EMSO) algorithm. It formulates the multiway partitioning problem as the 0-1 integer linear programming (ILP) problem, which is solved through the proposed EMSO algorithm adopting the multiway graph partitioning-based technique.
The mixed wireless sensor networks that are composed of a mixture of mobile and static sensors are the tradeoff between cost and coverage. To provide the required high coverage, the mobile sensors have to move from dense areas to sparse areas. The paper “An energy-efficient motion strategy for mobile sensors in mixed wireless sensor networks” presents a centralized algorithm to assist the movement of mobile sensors. The management node of the WSN collects the geographical information of all of the static and mobile sensors. The management node executes the algorithm to get the best matches between mobile sensors and coverage holes.
With the advance of embedded sensing devices, pervasive urban sensing (PUS) with probe vehicles is becoming increasingly practical. A probe vehicle is equipped with onboard sensing devices that detect urban information as the probe vehicle drive across the road network. In the paper “Pervasive urban sensing with large-scale mobile probe vehicles,” the authors present the framework of Pervasive Urban Sensing with probe vehicles, and showcase two cases of urban sensing with probe vehicles.
Rotating machinery is widely used in modern industry. It is one of the most critical components in a variety of machinery and equipment. Along with the continuous development of science and technology, the structures of rotating machinery become of larger scale, of higher speed, and more complicated, which results in higher probability of concurrent failure in practice. In the paper “Concurrent fault diagnosis for rotating machinery based on vibration sensors”, the authors develop an integrated method using artificial immune algorithm and evidential theory to achieve concurrent fault diagnosis for rotating machinery.
The paper “Enhanced mobile multiple-input multiple-output underwater acoustic communications” focuses on mobile multiple-input multiple-output (MIMO) underwater acoustic communications (UAC) over double-selective channels subject to both intersymbol interference and Doppler scaling effects. Under the assumption that the channels between all the transmitter and receiver pairs experience the same Doppler frequency, a variation of the recently proposed generalization of the sparse learning via iterative minimization (GoSLIM) algorithm is employed to estimate the frequency modulated acoustic channels.
Often, a large number of wireless sensor nodes are deployed to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. The paper “Weight-based clustering decision fusion algorithm for distributed target detection in wireless sensor networks” proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor networks.
Sensor network positioning systems have been extensively studied recently. How to acquire the anchor's position is a challenge. To address this issue, in the paper “Efficient deterministic anchor deployment for sensor network positioning,” the authors design an efficient mapping algorithm between anchors and their positions (MD-SKM) to avoid the complicated artificial calibration and propose a best feature matching (BFM) method to further relax the restriction of MD-SKM where three or more calibrated anchors are needed.
In vehicular networks, the multihop message delivery from information source to moving vehicles presents a challenging task due to many factors, including high mobility, frequent disconnection, and real-time requirement for applications. In the paper “Moving target oriented opportunistic routing algorithm in vehicular networks,” the authors propose a moving target oriented opportunistic routing algorithm in vehicular networks for message delivery from information source to a moving target vehicle. In order to adapt the constantly changing topology of networks, the forwarding decisions are made locally by each intermediate vehicle based on the trajectory information of the target vehicle.
With the increase of the storage capacity, computing, and wireless networking of the vehicular embedded devices, the vehicular networks bring a potential to enable new applications for drivers and passengers in the vehicles. In the paper “RoadGate: mobility-centric roadside units deployment for vehicular networks,” the authors study the problem of deploying the RSUs to provide the desired connectivity performance while minimizing the number of the deployed RSUs. Besides, the authors analyze a realistic vehicle trace, observe the mobility pattern, and propose a graph model to characterize it. Based on the graph model, the gateway deployment problem is transformed into a vertex selection problem in a graph. A heuristic algorithm RoadGate is then proposed to search greedily the optimal positions.
Optimizing the path planning to reduce the time and cost is an essential consideration in modern society. Using dynamic path planning to adjust and update the path information in time is a challenging approach to reduce road congestion and traffic accidents. In the paper “Data processing and algorithm analysis of vehicle path planning based on wireless sensor network,” the authors present a data analysis algorithm that determines an efficient dynamic path for vehicle repair-scrap sites and navigates more flexibly to avoid obstacles. The key idea is to design the wireless sensor network that helps to obtain data from different devices.
The paper “Trajectory-based optimal area forwarding for infrastructure-to-vehicle data delivery with partial deployment of stationary nodes” proposes a trajectory-based optimal area forwarding (TOAF) algorithm tailored for multihop data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles (infrastructure-to-vehicle) in vehicular ad hoc networks (VANETs) with partial deployment of stationary nodes. It focuses on reducing the delivery-delay jitter and improving the low reliability of infrastructure-to-vehicle communication.
The design and analysis of routing algorithms are important issues in WSNs. In the paper “An overlapping clustering approach for routing in wireless sensor networks,” the authors propose a k-connected overlapping clustering approach with energy awareness, namely, k-OCHE, for routing in WSNs. The basic idea of this approach is to select a cluster head by energy availability (EA) status. The k-OCHE scheme adopts a sleep scheduling strategy of CKN, where neighbors will remain awake to keep it k-connected, so that it can balance energy distributions well.
WSNs are important parts of Internet of Things or cyber-physical systems. Data query processing is very important for WSNs. In the paper “Continuous top-k contour regions querying in sensor networks”, the authors propose a Continuous Top-k Contour Regions Querying algorithm which can continuously obtain the top-k contour regions and does not lose the rate of precision. This technique takes full advantage of the kth value of top-k result in current round as the threshold to suppress the nodes whose readings do not belong to the top-k result in next round.
In WSNs, homogeneous or heterogeneous sensor nodes are deployed at a certain area to monitor our curious target. The sensor nodes report their observations to the base station (BS), and the BS should implement the parameter estimation with sensors’ data. Best linear unbiased estimation (BLUE) is a common estimator in the parameter estimation. In some soft real-time applications, we expect that the estimation can be completed before the deadline with a probability. In the paper “Energy-efficient soft real-time scheduling for parameter estimation in WSNs,” the authors proposed an energy-efficient scheduling algorithm especially for the soft real-time estimations in WSNs. Through the proper assignment of sensors’ state, an energy-efficient estimation is achieved before the deadline with a probability.
WSNs have limited energy and transmission capacity, so data compression techniques have extensive applications. For multivariate stream on a sensor node, some data streams are elected as the base functions according to the correlation coefficient matrix, and the other streams from the same node can be expressed in relation to one of these base functions using linear regression. By designing an incremental algorithm for computing regression coefficients, in the paper “A self-adaptive regression-based multivariate data compression scheme with error bound in wireless sensor networks,” the authors propose a multivariate data compression scheme based on self-adaptive regression with infinite norm error bound.
The drive-thru Internet is an effective mean to provide Internet access service for WSNs deployed on vehicles. In these networks, vehicles often experience different link qualities due to different relative positions to the access point. This makes fair and efficient system design a very challenging task. In the paper “Amortized fairness for drive-thru internet,” the authors propose a novel amortized fairness MAC protocol. Basically, vehicles with lower link quality can defer their fairness requests and let the lost fairness be “amortized” in the future when their links become the high quality. The inner and inter-AP correlations revealed from our extensive field studies are fully exploited, and a link quality prediction algorithm is proposed. Based on the predicted link quality, the optimal amortized fairness MAC is formulated as a convex programming problem, which can be solved with the desired precision in polynomial time.