Unknown tag identification is an important issue in large-scale RFID systems especially in automatic inventory control and asset tracking. Unknown tags refer to the tags, which have entered into the surveillance area, but not been identified by the reader yet. Many protocols have been proposed for unknown tag identification. However, they still suffer from a couple of drawbacks. Firstly, most of them leverage the classical EDFSA protocol in the identification phase, which is considerably time-consuming. Secondly, they do not filter known tags out so that known tags interfered with the unknown tag ID transmission process, especially when the density of known tags is high. To this end, we propose TIP, which is a time-efficient identification protocol for unknown RFID tags using Bloom filter. To avoid the interference of known tags to unknown tags, we employ the bloom filter to deactivate known tags. To make TIP protocol be time-efficient, we introduce an indicator vector for unknown tags so that each unknown tag transmits its ID only once during the holistic identification process. Extensive experimental results demonstrate that the proposed protocol achieves about 12% up to 74% time reduction during the identification process when the density of unknown tags ranges from 1% to 67%, compared with the existing protocols.

Traffic lights in urban area can significantly influence the efficiency and effectiveness of transportation. The real-time scheduling information of traffic lights is fundamentally important for many intelligent transportation applications, such as shortest-time navigation and green driving advisory. However, existing traffic light scheduling identification systems either entail dedicated infrastructures or depend on specialized traffic traces, which hinders the popularity and real world deployment. Differently, we propose to identify real-time traffic light scheduling by analyzing taxi traces that are widely accessible from taxi companies. The key idea is to exploit the periodicity in traffic patterns, which is directly affected by traffic lights. We also develop advanced algorithms to identify red/green lights duration and signal change time. We evaluate our solution using over one billion taxi records from Shenzhen, China. The evaluation results validate the effectiveness of our system.

A software-defined network empowers the creation of a flexible network architecture by abstracting flow control from individual devices to the network level. In this paper, we address the challenges in applying SDN to develop high- performance vehicular networks. We present SDVN, a new SDN based vehicular network architecture. It organizes the topology of the vehicular networks and utilizes vehicle trajectory prediction to mitigate the overhead of the SDN control and data plane communication. Moreover, we propose a multicast protocol over SDVN, as multicast is the foundation of many vehicular network applications. The protocol exploits the network topology information provided by SDVN to make far more efficient multicast scheduling decision. The multicast scheduling problem is formulated to minimize the communication cost with bounded delay constraint. A polynomial time approximation algorithm is proposed. We conduct extensive experiments using traffic traces. The evaluation shows that the SDVN based multicast protocol outperforms existing decentralized approaches.

It is critical to design energy-efficient communication technologies for wireless nanosensor networks (WNSNs) as nanosensors are highly energy-constrained. This paper focuses on WNSNs adopting the on-off keying (OOK) modulator. So far, some existing low-weight (LW) codes with low average codeword weight (ACW) map source symbols into different codewords with fewer high bits so as to greatly reduce the transmission energy at the transmitter. However, the transmission energy reduction is achieved at the price of large reception energy at the receiver as the codeword lengths of LW codes are large, incurring their ineffectiveness in most scenarios. To remedy the problem, we design the fixed-length minimum-communication-energy (F-MCE) code and variable-length minimum-communication-energy (V-MCE) code to minimize the total energy consumption at the transmitter and receiver for point-to-point communication in OOK-based WNSNs. Specifically, the code design problems are formulated as integer nonlinear programming (INLP) problems, and the F-MCE and V-MCE codes are obtained by solving the INLP problems. The F-MCE and V-MCE codes are applicable in more scenarios than the LW code. Extensive experimental results show that the V-MCE always outperforms the LW code regarding the energy saving, while the F-MCE code achieves energy saving no less than that of the existing LW codes.

Abstract Radio Frequency Identification (RFID) technology provides a seamless link between physical world and the information system in cyber space. However, the emerging Internet of Things and cloud systems are full of security holes, which introduce new challenging security problems in both tag identification and data privacy. In this paper, we present efficient methods for identity authentication and data encryption to enhance RFID privacy. The proposed techniques aim to ensure no adversary interacting with the tags and the reader, to infer any information on a tag's identity from the communication. The main idea, a symmetric cryptography technique, termed as Advanced Encryption Standard, was applied to implement both mutual authentication and data encryption between the front and back ends of RFID systems. The Advanced Encryption Standard cryptographic algorithm was adopted because of its low hardware complexity and high security strength. In addition, the proposed key management protocol is proved resistant to several known RFID attacks such as Man‐in‐the‐Middle, Denial‐of‐Service, replay, clone attack, and backward/forward traceability. The computational time complexity of the proposed scheme through the entire identity authentication and data encryption phases is 3Tencrypt + 1Tnonce + 4Txor, which is superior to most existing approaches. The proposed scheme was also proved to be able to provide higher data encryption performance than other symmetric cryptographic algorithms with regard to the same level of security strength. Copyright © 2016 John Wiley & Sons, Ltd.

The multidisciplinary nature and time criticality of computing in Cyber-Physical Robot Systems (CPRS) makes it significantly different from traditional computer systems. This article attempts to create a usage-specific language called Cyber-Physical Robot Language (CPRL), which supports the CPRS design and implementation in an integrative and swift way. Multiview description and integration strategies as well as formal execution semantics for usage-specific simulation and verification are outlined. A graphic unified environment for CPRS modeling is supplied, in which several tools are integrated. A 6-DOF distributed robot system development in the environment is presented. The approach is an attempt to support CPRS design in an effective way, at the same time guaranteeing the system function and performance requirements.

With the urban development and enlargement, various regions such as residential zones and administrative districts now appear as parts of cities. People exhibit different mobility patterns in each region, which is closely relevant to region-wide functions. In this article, we propose a scheme to discover region-wide functions using large-scale Shanghai taxicab trajectories that capture enormous traces for more than 13,000 taxicabs over a period of about 3 years. We investigate these taxicab trajectories and conduct an extensive preliminary study. Then, we divide the city into disjointed regions using Voronoi decomposition. By incorporating people's pick-up and drop-off information, we refine the Voronoi partitioning results to identify region-wide functional areas. Finally, we study people's movement frequency on weekdays and weekends for every kind of urban functional regions. We also look into human mobility within or across the identified urban functional regions. Experimental results show that human movement is bounded with the function of urban regions, and more than 90% of people visit neighboring (less than 20km travel distance) functional regions with high probability.

Location prediction based on cellular network traces has recently spurred lots of attention. However, predicting user mobility remains a very challenging task due to the fuzziness of human mobility patterns. Our preliminary study included in this paper shows that there is a strong correlation between the calling patterns and co-cell patterns of users (i.e., co-occurrence in the same cell tower at the same time). Based on this finding, we propose NextCell—a novel algorithm that aims to enhance the location prediction by harnessing the social interplay revealed in cellular call records. Moreover, our proposal removes the assumption held in previous schemes that binds locations of cell towers to concrete physical coordinates, e.g., GPS coordinates. We validate our approach with the MIT Reality Mining dataset that involves 32,579 symbolic cell tower locations and 350,000 hours of continuous activity information. Experimental results show that NextCell achieves higher precision and recall than the state-of-the-art schemes at cell tower level in the forthcoming one to six hours.

The Internet of Things (IoT) opens up tremendous opportunities to location-based industrial applications that leverage both Internet-resident resources and phones' processing power and sensors to provide location information. Location-based service is one of the vital applications in commercial, economic, and public domains. In this paper, we propose a novel localization scheme called NextMe, which is based on cellular phone traces. We find that the mobile call patterns are strongly correlated with the co-locate patterns. We extract such correlation as social interplay from cellular calls, and use it for location prediction from temporal and spatial perspectives. NextMe consists of data preprocessing, call pattern recognition, and a hybrid predictor. To design the call pattern recognition module, we introduce the notions of critical calls and corresponding patterns. In addition, NextMe does not require that the cell tower addresses should be bounded with concrete coordinates, e.g., global positioning system (GPS) coordinates. We validate NextMe across MIT Reality Mining Dataset, involving 500 000 h of continuous behavior information and 112 508 cellular calls. Experimental results show that NextMe achieves fine-grained prediction accuracy at cell tower level in the forthcoming 1-6 h with 12% accuracy enhancement averagely from cellular calls.

In this paper we propose a novel Frequency Selective SRS (FS-SRS) scheme to improve the quality of CSI. According to the CQIs (channel quality indications), which are periodically reported by each user, and the bandwidth requirement of each user, BSs dynamically schedule each user to send SRSs only on the requisite bandwidth with best CQIs instead of on full bandwidth or on specified sub-bandwidth as in the current LTE TDD systems. Simulation results show that the proposed FS-SRS scheme outperforms the current schemes in estimation accuracy for various frequency-selective and/or time-selective fading channels. In addition, the proposed FS-SRS scheme offers unique advantages over the current schemes. Firstly, it is robust to frequency selective channels. Secondly, it is hardly impacted by timing offset. Thirdly, it is adaptive to the change of the underlying scheduler. Fourthly, it can increase SRS capacity. Finally, it can be extended to multi-user MIMO (multiple-input multiple-output) systems, called extended FS-SRS scheme. Moreover, the proposed schemes are applicable to the uplink SRS design in LTE-Advanced systems.

With the recent technical advances and popularization of smartphones, which are able to store, display, and transmit various types of media content, message forwarding in opportunistic mobile social networks has become a hot topic. In this paper, we propose a social-aware single-copy routing approach, which leverages the internal social feature information to resolve the social distance between the source and destination step-by-step. We introduce an optimal social feature forwarding set selection scheme for routing guidance, which is proven to achieve small message forwarding delay. We convert the routing process into an iterative 2-hop routing scheme, with a novel transition probability to measure the delivery delay of each hop. The transition probability is according to the community structure in each social feature space. Extensive simulations on both real and synthetic traces are conducted in comparison with several existing state-of-art approaches.

In wireless network, the resource limitations and process speed are the main constraints. They make many traditional background subtraction methods unable to be applied in wireless network. Vibe algorithm is a newly proposed background subtraction technique and its computational load is very low. That makes it available in the wireless network. However, Vibe is a pixel-level algorithm and it does not take the information of region into consideration. So it can be easily influenced by the background disturbance, noise and illumination change. In this paper a novel background subtraction algorithm which can be used in wireless network is proposed. To deal with these problems, the proposed algorithm employs graph-based image segmentation to segment every frame and construct the region-level information. With the region-level information, the proposed algorithm provides several strategies such as classification of regions, modification in neighboring regions and fast elimination of ghost region to improve the result of Vibe algorithm. Experiment results demonstrate that the proposed method can effectively extract the moving objects in various categories of scenes.

Vehicular Ad hoc Networks (VANETs) are a killer application of Mobile Ad hoc Networks (MANETs), which exchange data among vehicles and vehicles to roadside infrastructures by routing. To save energy, various routing protocols for VANETs have been proposed in recent years. However, VANETs impose challenging issues to routing. These issues consist of dynamical road topology, various road obstacles, high vehicle movement and the fact that the vehicle movement is constrained on roads and traffic conditions. Moreover, the movement is significantly influenced by driving behaviors and vehicle categories. To this end, we incorporate them into routing and propose energy-efficient routing using movement trends (ERBA) for VANETs—an energy-efficient routing protocol. ERBA classifies vehicles into several categories, and then leverages vehicle movement trends to make routing recommendation. It predicts the movement trends by current directions and next directions after going through the road intersections. With the vehicular category information, the driving behavior patterns, the distance between the current sections and the next intersections, ERBA propagates information among vehicles with less energy consumption. The proposed scheme is validated by real urban scenarios extracted from ShanghaiGrid project. Experimental results show that ERBA outperforms the compared routing protocols with respect to the end-end delay, the packet delivery ratio and the path duration time.

Inter-contact time (denoted as TI) between mobile nodes that captures the temporal characteristics of Vehicular Ad Hoc Networks (VANETs) has been intensively studied. Whereas the node spatial distribution is ignored in most existing mobility schemes, which is also worthy of investigation, particularly for real applications. Moreover, the node spatial distribution has a significant influence on the inter-contact time. In this paper, we study the empirical data acquired from taxis in Shanghai city. We find that most taxis distribute on some hot roads which makes the node spatial distribution appear power law. Based on this observation, we propose the concepts of indirect contact and heterogeneous inter-contact time (represented as TH) to reveal how hot roads can change the distribution of inter-contact time. By investigating the empirical data, we show that the THdistribution also appears power law.

Location prediction using mobile phone traces has attracted increasing attention. Owing to the irregular user mobility patterns, it still remains challenging to predict user location. Our empirical study in this paper shows that the call patterns are strongly correlated with co-locate patterns (i.e., visiting the same cell tower at the same period), and the call patterns mainly affect user short-time mobility. On top of these findings, we propose NextMe --- a novel scheme to enhance the location prediction accuracy by leveraging the social interplay revealed in the cellular calls. To identify when the social interplay will affect user mobility, we introduce the concepts of the Critical Call Pattern (CCP), and the Critical Call (CC). We validate NextMe with the MIT Reality Mining dataset, involving 350,000-hour activity logs of 106 persons, and 112,508 cellular calls. Experimental results show that the social interplay significantly improves the accuracy.

This paper discusses the challenges in searching imposed by the burgeoning field of Internet of Things (IoT). It first overviews the evolution of the new field to its predecessors: searching in the mobile computing, ubiquitous computing and information retrieve. Then, it identifies four research thrusts: architecture design, search locality, real-time and scalability. It also sketches several presumptive IoT scenarios, and uses them to identify key capabilities missing in today's systems. This paper concludes with a discussion of the research necessary to design and develop these capabilities. In addition, this paper proposes a series of measurement dimensions, based on which it investigates the state-of-the-art efforts.

Radio Frequency IDentification (RFID) has attracted considerable attention in recent years for its low cost, general availability, and location sensing functionality. Most existing schemes require the tracked persons to be labeled with RFID tags. This requirement may not be satisfied for some activity sensing applications due to privacy and security concerns and uncertainty of objects to be monitored, e.g., group behavior monitoring in warehouses with privacy limitations, and abnormal customers in banks. In this paper, we propose TASA-Tag-free Activity Sensing using RFID tag Arrays for location sensing and frequent route detection. TASA relaxes the monitored objects from attaching RFID tags, online recovers and checks frequent trajectories by capturing the Received Signal Strength Indicator (RSSI) series for passive RFID tag arrays where objects traverse. In order to improve the accuracy for estimated trajectories and accelerate location sensing, TASA introduces reference tags with known positions. With the readings from reference tags, TASA can locate objects more accurately. Extensive experiment shows that TASA is an effective approach for certain activity sensing applications.

Collaborative filtering (CF) has achieved widespread success in recommender systems such as Amazon and Yahoo! music. However, CF usually suffers from two fundamental problems - data sparsity and limited scalability. Among the two broad classes of CF approaches, namely, memory-based and model-based, the former usually falls short of the system scalability demands, because these approaches predict user preferences over the entire item-user matrix. The latter often achieves unsatisfactory accuracy, because they cannot capture precisely the diversity in user rating styles. In this paper, we propose an efficient collaborative filtering approach using smoothing and fusing (CFSF) strategies. CFSF formulates the CF problem as a local prediction problem by mapping it from the entire large-scale item-user matrix to a locally reduced item-user matrix. Given an active item and a user, CFSF dynamically constructs a local item-user matrix as the basis of prediction. To alleviate data sparsity, CFSF presents a fusion strategy for the local item-user matrix that fuses ratings of the same user makes on similar items, and ratings of like-minded users make on the same and similar items. To eliminate diversity in user rating styles, CFSF uses a smoothing strategy that clusters users over the entire item-user matrix and then smoothes ratings within each user cluster. Empirical study shows that CFSF outperforms the state-of-the-art CF approaches in terms of both accuracy and scalability.

Previous architectures of pervasive computing are customized for specific types of applications. In this paper, we propose a new architecture named iShadow, which facilitates the design and implementation of generic applications in pervasive computing environment. iShadow gracefully integrates physical spaces and human attention, and provides fundamental and flexible support to construct pervasive applications rapidly. Significant differences of iShadow from previous works are lightweight user-shadow model, scalable resource discovery and potent context inference mechanism. Our prototypes demonstrate that the iShadow architecture is robust, feasible and effective for pervasive applications.