We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention modules are employed to select the most important local features that best describe an indoor place. Our AEGIS-Net is made of a semantic encoder, a semantic decoder and an attention-guided feature embedding. The model is trained in a 2-stage process with the first stage focusing on an auxiliary semantic segmentation task and the second one on the place recognition task. We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods. Our AEGIS-Net achieves exceptional performance and outperforms all six methods.

Social online video has emerged as one of the most popular application, where “bullet screen comment” is one of the favorite features of Asian users. User behavior report finds that most people are used to quickly navigate and locate his concerned video clip according to its corresponding video labels. Traditional scene segmentation algorithms are mostly based on the analysis of frames, which cannot automatically generate labels. Since time-synchronized comments can reflect the episode of current moment, this paper proposed an unsupervised video episode boundary detection model (VEBD) for bullet screen comment video. It could not only automatically identify each episode boundary, but also detect the topic for video tagging. Specifically, a Joint Episode-Topic model is first constructed to detect the hidden topic in initial partitioned time slices. Then, based on the detected topics, temporal and semantic relevancy between adjacent time slices are measured to refine the boundary detection accuracy. Experiments based on real data show that our model outperforms the existing algorithms in both boundary detection and semantic tagging quality.

Mobile crowdsourcing (MCS) is now an important source of information for smart cities, especially with the help of unmanned aerial vehicles (UAVs) and driverless cars. They are equipped with different kinds of high-precision sensors, and can be scheduled/controlled completely during data collection, which will make MCS system more robust. However, they are limited to energy constraint, especially for long-term, long-distance sensing tasks, and cities are almost too crowded to set stationary charging station. Towards this end, in this paper we propose to leverage emerging deep reinforcement learning (DRL) techniques for enabling model-free unmanned vehicles control, and present a novel and highly effective control framework, called “DRL-RVC.” It utilizes the powerful convolutional neural network for feature extraction of the necessary information (including sample distribution, traffic flow, etc.), then makes decisions under the guidance of the deep Q network. That is, UAVs will cruise in the city without control and collect most required data in the sensing region, while mobile unmanned charging station will reach the charging point in the shortest possible time. Finally, we validate and evaluate the proposed framework via extensive simulations based on a real dataset in Rome. Extensive simulation results well justify the effectiveness and robustness of our approach.

Participatory Crowdsourcing System (PCS) is now an important source of information for big data applications. In PCS, required environmental parameters are collected and shared by ordinary citizens. And the limited range of human activities and limited power or capability of smart devices make the PCS facing a lot of problems. But the wide use of robots, such as Unmanned Aerial Vehicle (UAV), can effectively alleviate the current problems in PCS. Lots of precision sensors can be installed into the robots, which makes sensory data collected by robots more trustful. However, the power and data transmission capability of robots are also limited. Meanwhile, it is much more expensive to use robots than recruit human participants. Thus, it will be most useful and necessary to use robots in PCS, let the robots and human participants working together, rather than using robots to replace the human participants. Obviously, the cooperation of robots and human participants will also make PCS facing new challenges, such as how to use the least amount of robots to reduce costs, etc.. In this paper, we analyze the different application scenarios of robots-aided PCS, and propose possible solutions to the existing problems. Finally, we discuss possible future work for robots-aided PCS.

Crowdsourcing systems, by using smart devices like smartphones and iPad, have been widely used in various domains, but are currently facing new challenges. On one hand, different tasks offer different amount of incentive budgets in multitask systems, thus those tasks pay more should be satisfied preferentially. On the other hand, even if there are not enough budget for a sensing task, the system should also try to provide as much sensory data as possible to obtain potential clients. Furthermore, it is challenging to select an “optimal” set of participants as data contributors due to the above two points. In this paper, first, we introduce a metric to describe how the collected sensory data can quantify task's the multi-dimensional data requirements, in terms of data distribution in spatiotemporal domains. Second, we propose a “task priority model” based on incentive budget, to explicitly quantify the relationship between the incentive budget usage and task priority. Then, we present a quality of information (QoI) aware participant selection approach as a suboptimal solution to the defined optimization problem. Finally, we compare our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach.

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants’ handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users’ incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users’ incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.

Incentive allocation is an important research issue in participatory sensing as it determines the willingness of participants in joining the sensing campaign. Existing incentive approaches either decide the payments without consulting the participants, or require burdensome negotiation procedures like bid-price auction. In this paper, we propose a novel incentive allocation mechanism, which encourages participation and allocates incentives dynamically to achieve accurate sensing results. The proposed mechanism consists of two major elements. The first is a lightweight incentive negotiation procedure, which dynamically offers incentives to participants in spatio-temporal subregions and collects their responses. The second is the optimization problem for incentive allocation as well as it's solution, which aims at maximizing data quality by capturing the amount and the distribution of data samples. Simulations with real datasets confirmed that the proposed solution can provide dynamic incentive offers according to the estimated value of participants' data contribution to the overall quality of sensing result.

In increasingly popular participatory sensing systems, new challenges are arising to select the most appropriate participants when considering their hand-held smart device's different energy conditions, uncontrollable mobility pattern, and associated sensing capabilities to best satisfy the quality-of-information (QoI) requirements of sensing tasks. This paper proposes a QoI-aware energy-efficient participant selection strategy, where four key design elements are proposed. First is QoI satisfaction metric of a sensing task that uses the data granularity and quantity collected by participants to measure to what extend the task's QoI requirements are satisfied. Second is an “energy consumption index”, which estimates the impact of energy cost during the data collection on different participant's smart devices with different remaining energy levels. Third is the estimation of the collected amount of data by participants, where a probability-based movement model is proposed. Fourth is the proposal of a multi-objective constrained optimization problem for participant selection, where task QoI requirements and energy consumption index of all participants are taken as optimization objectives, and solved by our proposed suboptimal, easy-to-implement solution. Real and extensive trace-based experiments show that, the proposed participant selection scheme can well balance the trade-off between the task QoI and energy consumptions by selecting most efficient participants, compared with existing schemes.

In some practical scenarios such as tour guiding and children babysitting, one mobile device held by tour guides or parents need to know the distance and direction of another nearby mobile device held by tourist or children. However, to date, most existing pedestrian localization methods rely on a fixed external infrastructure, such as a global positioning system(GPS) or pre-deployed wifi access points to provide Localization service for mobile devices. Such methods are constrained either by limited GPS coverage or by complicated set-up procedures. We observe that, when two devices are moving, the change of their positions leads to the change of distance between them. Given the same movements, different relative locations between devices lead to different distance changes. Besides, the distance between and relative movement of devices can be measured by two phone-embedded sensors respectively. This motivates us to exploit the relative localization method by merely two mobile devices. In this work, we present Phone-Radar, which is an infrastructure-free device-to-device localization system. According to the propagation model of wireless signals, the change of distance between devices are modeled by the change of wireless signal strength between them. The movements of devices are recorded by the inertial sensors using step-counting method. We further study the relationship among the initial relative locations between the two devices, their relative movements and the change of received signal strength measurements. Moreover, we implement the proposed method and measure its performance under real world conditions. The testbed experiments show the efficiency of our proposed method.

Incentive strategy is important in participatory sensing, especially when the budget is limited, to decide how much and where the samples should be collected. Current auction-based incentive strategies purchase sensing data with lowest price requirements to maximize the amount of samples. However, such methods may lead to inaccurate sensing result after data interpolation, particularly for participants that are massing in certain subregions where the low-price sensing data are usually aggregated. In this paper, we introduce weighted entropy as a quantitative metric to evaluate the distribution of samples and find that the distribution of data samples is another important factor to the accuracy of sensing result. We further propose a greedy-based incentive strategy which considers both the amount and distribution of samples in data collection. Simulations with real datasets confirmed the impact of samples distribution to data accuracy and demonstrated the efficacy of our proposed incentive strategy.

Wi-Fi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing Wi-Fi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, Wi-Fi clients must actively discover new Wi-Fi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFithat utilizes ZigBee radios to identify the existence of Wi-Fi networks through unique interference signatures generated by Wi-Fi beacons. We develop a new digital signal processing algorithm called common multiple folding (CMF) that accurately amplifies periodic beacons in Wi-Fi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of Wi-Fi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect Wi-Fi APs with high accuracy (<;5 percent total FP and FN rate), short delay (~780 ms), and little computation overhead.

The Vehicular Ad hoc Networks (VANETs) have been recently introduced to provide high-speed Internet access to vehicles by deploying 802.11 enhanced Roadside Units (RSUs) along roads. However, few content downloading oriented RSU deployment strategies have been proposed. In this paper, we propose a new RSU deployment strategy for content downloading in VANETs. The encounters between vehicles and RSUs are modeled as a time continuous homogeneous Markov chain. The optimal inter-meeting time between vehicles and RSUs is analyzed based on the encounter model. Then, the road network is modeled as a weighted undirected graph, and a RSU deployment algorithm is designed based on the depth-first traversal algorithm for edges of a graph. Simulation results show that the proposed RSU deployment algorithm can satisfy the file downloading service requirements with the lowest RSU deployment cost.

This paper presents a cross-platform solution of smartphone-based mobile sink for wireless sensor networks, named uSink. With a cross-platform SD card, named uSD card, any smartphone with SD interface can be empowered with the capability to communicate with wireless sensor nodes. Furthermore, a middleware on mobile phone, named uSinkWare, is also designed to provide a typical mobile sink's functionalities, including sensor detection, topology monitoring, routing, sensor data collection and sensor control, etc. Thanks to Qt, uSinkWare can also work on many popular mobile platforms, such as Symbian, Maemo, Windows mobile and embedded Linux. uSink has been implemented, and its performance has been verified by experiments. The experiment results show that current high-end smartphones have enough capabilities to work as mobile sinks, and uSink can be regarded as an attempt on this aspect.

Wireless ad hoc networks can experience significant performance degradation under fading channels. In this work, we present a robust forwarding extension (RFE) for reactive routing protocols in wireless ad hoc networks. RFE is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links. Specifically, we introduce a biased backoff scheme during the route discovery phase to find a robust virtual path, which can provide more cooperative forwarding opportunities. Along this virtual path, data packets are greedily progressed toward the destination through nodes cooperation. We extend the widely used AODV routing protocol with RFE to study its performance. Through extensive simulations, we demonstrate that AODV-RFE effectively improves the reliability, end-to-end energy efficiency and latency.

NA

A VANET consists of vehicles equipped with on board units (OBUs) that can communicate with each other and the road side base stations. Due to the mobility and sparse distribution of vehicles, the delivery delay of messages in the VANET is mainly caused by the message transmissions between vehicles. The message delivery delay directly impacts the deployment of applications in VANET, and hence, an in-depth study of message delivery delay in the VANET is significant. In this paper, we focused on the investigation of the message delivery delay in the V2V stage with a bidirectional setting. The bidirectional traffic was modeled as a combination of two Poisson point processes. Based on the sub-additive ergodic theory, we found theoretically that the message delivery delay has a linear relationship with the message forwarding distance. Further, the upper bound of the coefficient of the linear relationship has an exponential polynomial relation with the density of vehicles on the road and decreases with the increment of the velocity of the traffic.

Location Based Notification (LBN) aims to alert the users in a target area with the information of interest to them. With a wide range of applications, LBN has been gaining more and more attraction among wireless users and service providers. The mainstream centralized solution based on cellular networks may incur high service cost. In this paper, we present an innovative scheme called Phoenix, which does not rely on any infrastructure, to implement for location based notification service. In our design, devices (users) across the target area form a dynamic peer-to-peer network, where a user can be a message source, a message carrier, or a message subscriber. When a user meets the message carrier, the user can get a copy of the message. Phoenix keeps messages of interest being circulated in the target area, hence users are being notified. To achieve desired notification performance, Phoenix adaptively controls when a user should take the carrier role and help disseminating a message in order to keep the message ``alive", given the fact that message carriers may leave the target area and drop the message. Extensive simulations have been conducted to show the efficacy of Phoenix notification system.

It has been shown that sink mobility provides an energy-efficient approach to data delivery in wireless sensor networks (WSNs). Most of the approaches targeted to WSNs with mobile sinks (MSs) addressed the problem of data delivery where only a few messages are reported during a long time frame. However, transmitting streaming data is becoming relevant in WSNs, as more and more multimedia sensor nodes - equipped with image, audio, and video capabilities - are being used to characterize the sensing environment. In this scenario, a sequence of messages propagates into the network, hence the problem of finding an effective routing path for delivering data to MSs becomes even more challenging, since the communication overhead for reaching the MS might also be significant. In this paper, we present an energy-efficient streaming data delivery (SDD) protocol for cluster-based WSNs with MSs. Different from existing works, we focus on the mobility support for the delivery of streaming data in hierarchical WSNs. By introducing a cross-cluster handover mechanism and a path redirection scheme, SDD maintains the end-to-end connectivity between the source and the MS, while avoiding the constant transmission of the MS location as it moves across multiple clusters. We evaluate the performance of the proposed SDD protocol, and compare it with a hierarchical cluster-based data dissemination protocol. Simulation results demonstrate its effectiveness, in terms of both end-to-end delivery delay and energy-efficiency.

Contention-based geographic forwarding (CGF) is a state-free communication paradigm for data delivery in multihop wireless sensor networks. CGF is robust to frequent topology changes, scalable to large-scale node deployment and applicable to data-centric applications and resource constrained networks. However, CGF may experience significant performance degradation under unreliable links. In this work, we present the intelligent CGF (ICGF) to combat the channel variation. IGCF combines the advantages of both cooperative and contention-based forwarding, involving multiple neighbours of the sender into the local forwarding to improve the transmission reliability. ICGF differs from existing work in that it extends the cooperation scope intelligently, by sending one additional control message on demand. For this reason, the probability of cooperation void in ICGF is decreased and the single-hop packet progress is increased. The authors conduct extensive simulations to study the performance of the proposed ICGF compared with existing protocols. Simulation results demonstrate that ICGF improves the end-to-end data delivery delay, energy efficiency and data delivery ratio.

How to organize and visualize big amount of text messages stored on one's mobile phone is a challenging problem, since they can hardly be organized by threads as we do for emails due to lack of necessary metadata such as "subject" and "reply-to". In this paper, we propose an innovative approach based on clustering algorithms and natural language processing methods. We first cluster the text messages into candidate conversations based on their temporal attributes, and then do further analysis using a semantic model based on Latent Dirichlet Allocation (LDA). Considering that the text messages are usually short and sparse, we trained the model using a large scale external data collected from twitter-like web sites, and applied the model to text messages. In the end, the text messages are organized as conversations based on their topics. We evaluated our approach based on 122,359 text messages collected from 50 university students during 6 months.

Usually, sensor data needs to be disseminated from the source sensors to data collectors (e.g., sink nodes), making those spatially distributed sensor data available for applications to access. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDCs) to collect and deliver the sensor data. The goal of this work is to design a dissemination protocol that leads to efficient data delivery from the source sensors to ubiquitous MDCs. We propose the Wait-Focus-Spray (WFS) scheme for wireless sensor networks with ubiquitous MDCs. The main objective of WFS is to balance the data delivery latency and transmission overhead when considering the existence of ubiquitous MDCs. In WFS, we also propose a corresponding mechanism-probabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting a MDC. Through extensive simulations, we demonstrate that our WFS scheme reduces the transmission cost per message while provides comparable delivery delay compared with the existing work.

Energy efficient multicast routing is one of the fundamental problems in wireless sensor networks (WSNs). Previous work has shown that when the goal is to find multicast trees with minimum transmission cost, the problem becomes NP-complete. In this work, we present a heuristic distributed minimum transmission multicast routing protocol (MTMRP) for WSNs. By introducing the biased backoff scheme and taking advantage of the broadcast nature of wireless communication, MTMRP chooses the forwarding routes which can connect more multicast receivers. Moreover, MTMRP introduces a path handover scheme, which can prune redundant routes for multicast routing. As a result, the multicast transmission cost is reduced in a distributed manner. We conduct extensive evaluations to study the performance of the proposed MTMRP compared with existing protocols. Simulation results demonstrate that our scheme effectively improves the multicast routing energy efficiency.

A mobile opportunistic network consists of sparsely scattered mobile nodes communicating via short range radios. It is characterized by frequent and unpredictable network partitions and intermittent connectivity. Anycast in opportunistic networks is anticipated in many application scenarios, and deserves great attention. In this paper, we propose an anycast routing algorithm in which each node is associated with a forwarding metric indicating its delivery probability to the destination anycast group and the node with lower value hands over the message to the encountered node with higher metric. The forwarding metric is determined according to historical node encounter information. We use three different forwarding metrics (variables) to guide the transmission of messages. The Group Forwarding Metric (GFM) treats the entire group as a whole, and it is defined as the probability of meeting any member in the anycast group to deliver a message. Similar to GFM, Probability Forwarding Metric (PFM) is defined as the probability of encountering at least one anycast group member, but it relies on the probability of meeting individual group members. The Distance Forwarding Metric (DFM) takes a function of the delivery probability to an anycast group member as the distance to the member. The DFM is the combination of these distances to forward messages towards the higher member density. Different metrics can be adopted for different mobile opportunistic networks based on the connectivity characteristics of the networks. We analyze the control overhead of the anycast algorithm and the message delivery delay of the routing protocol. Extensive simulations are carried out to evaluate the performance of the proposed solution under synthetic and realistic traces. The results show that our algorithm will significantly improve the anycast delivery performance when compared with simple routing algorithms in term of average message delivery delay and transmission overhead.

A growing number of applications require disseminating information around specific geographical areas within a limited valid time. For example, the store in the mall area expects to publish the time-limited sales promotion to all the potential clients in the nearby area, before the discount activity end. In this paper, we study the problem of deploying infostation for geographical information dissemination. It aims to achieve the desired dissemination ratio under the given time constraint and to minimize the infostation deployment cost. Inspired by several observations in recent studies on realistic mobility model, we build a mobility graph to reflect the statistical characteristic of users movement in a area. Based on this graph, we formulate the infostation deployment problem as an optimization problem. Then, we prove it is NP-hard by reducing it to the classical vertex cover problem and then develop a greedy heuristic algorithm DGREEDY with the polynomial time complexity. Extensive simulations based on the real human mobility traces have been carried out to show the efficacy of our approach.

Cooperative forwarding has been considered as an effective strategy for improving the geographic routing performance in wireless sensor networks (WSNs). However, we observe that existing works do not fully utilize the forwarding opportunities provided by available neighboring nodes. In this paper, we redesign the cooperative forwarding process and present a novel cooperative contention-based forwarding (CCBF) protocol for WSNs. CCBF extends the scope of cooperation and attains the full potential of cooperative forwarding at the expense of sending one additional control message on demand. We conduct extensive simulations to study the performance of the proposed CCBF compared with existing protocols. Simulation results demonstrate that CCBF decreases the end-to-end hop counts, hence, further improves the end-to-end energy efficiency and latency. Remarkably, it provides up to 50% improvement for the packet loss ratio when the retransmission mechanism is not adopted.

In a pervasive computing environment, the devices are embedded in the physical world, providing services and interconnected by a communication network. Composition of these services is important issue of pervasive applications which integrate the physical and cyber worlds. Most existing research on service composition in pervasive computing relies on the existence of one or more entities that maintain the global service information. However, such an approach is not always practical due to dynamicity of the environment. In this paper, we propose a fully decentralized approach to service composition. We first model the service composition problem as finding an overlay of the communication network that matches the composition graph. The problem is proved to be NP-complete. We propose an algorithm for the devices to cooperatively construct the requested services through localized interactions. For the purpose of reducing redundant broadcast we propose the service composition backbone built in a fully localized way. We have carried out extensive simulations. to evaluate the performance of our algorithm. Compared with existing pull-based centralized techniques our decentralized service composition algorithm on the service composition backbone is more efficient in terms of response delay and message overhead, while achieving similar quality of composed service.

In this paper, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in wireless sensor networks (WSNs). Recent work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. We also find that existing GOR protocol may not be suitable for QoS provisioning in WSNs, due to the large computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we study the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. We look in depth at the properties of the multiple objectives. Based on the analysis and observations, we then propose a heuristic efficient GOR (EGOR) algorithm for QoS provisioning in WSNs. We evaluate EGOR by comparing it with the multipath routing approach through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate that EGOR can significantly improve both the end-to-end energy efficiency and latency for multiconstrainted QoS provisioning in WSNs, and that EGOR is characterized by its low time complexity.

WiFi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing WiFi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, WiFi clients must actively discover new WiFi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFi that utilizes ZigBee radios to identify the existence of WiFi networks through unique interference signatures generated by WiFi beacons. We develop a new digital signal processing algorithm called Common Multiple Folding (CMF) that accurately amplifies periodic beacons in WiFi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of WiFi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect WiFi APs with high accuracy (<5% total FP and FN rate), short delay (~780 ms), and little computation overhead

Considering sensor nodes deployed densely and uniformly a mobile sink moving through the sensing field queries a specific area of interest for monitoring information. The Query packet, injected by the mobile sink, is routed to the specific area and the corresponding Response packet is expected to return via multi-hop communication. In this paper, we analyze such a network model to address the problem of efficient data collection for mobile wireless monitoring applications. We first propose a meeting position-aware routing (MPAR) protocol for routing the Response packet efficiently and then propose an efficient query-based data collection scheme (QBDCS) for mobile wireless monitoring applications based on the MPAR. In order to minimize the energy consumption and packet delivery latency, the QBDCS chooses the optimal query time of injecting the Query packet and tailors the routing mechanism for sensor nodes forwarding packets. Simulation study has verified the analysis and demonstrated that the QBDCS can significantly reduce the energy consumption and end to end delivery latency.

Abstract In wireless sensor networks (WSNs), a mobile sink can help eliminate the hotspot effect in the vicinity of the sink, which can balance the traffic load in the network and thus improve the network performance. Location‐based routing is an effective routing paradigm for supporting sink mobility in WSNs with mobile sinks (mWSNs). To support efficient location‐based routing, scalable location service must be provided to advertise the location information of mobile sinks in an mWSN. In this paper, we propose a new hierarchical location service for supporting location‐based routing in mWSNs. The proposed location service divides an mWSN into a grid structure and exploits the characteristics of static sensors and mobile sinks in selecting location servers. It can build, maintain, and update the grid‐spaced network structureviaa simple hashing function. To reduce the location update cost, a hierarchy structure is built by choosing a subset of location servers in the network to store the location information of mobile sinks. The simulation results show that the proposed location service can significantly reduce the communication overhead caused by sink mobility while maintaining high routing performance, and scales well in terms of network size and sink number. Copyright © 2009 John Wiley & Sons, Ltd.

Utilizing mobile devices such as PDAs, cell phones to collect and deliver sensor data is considered as a promising application in future ubiquitous computing environment. In such application scenarios, the human factor is becoming increasingly important. In this paper, we propose a group-level incentive scheme targeted for encouraging mobile users to collect sensor data. Such idea of widening the scope of incentive from device- level to group-level is inspired by the incentive scheme of blood donation in real life. Mobile users earn credits by collecting and delivering sensor data with their mobile devices and they may consume the credits to access the services provided by wireless sensor networks applications. The main idea of the group-level incentive scheme is to group mobile users into social groups and share credits so that credits earned by one user can be consumed by another user in the same group. Through theoretical analysis and simulation we illustrate the potential of the proposed solution.

Efficient and reliable communications is a critical issue in wireless networks with lossy links. In this paper, we propose a neighbor-assisted coding aware opportunistic retransmission mechanism to increase the network throughput. The key idea behind our design is as follows. If a node fails to receive a packet due to link loss, its neighboring node(s) receiving the packet can assist the retransmission of the packet, possibly encoded with other packet(s) via localized network coding, if such retransmission is expected to be beneficial. This can effectively reduce the total number of packet retransmissions at the MAC layer. Simulation results show that our proposed mechanism can significantly increase the network throughput as compared with existing work.

In this paper, we design a destination-driven on-demand multicast routing protocol for wireless ad hoc networks. The design objective is to improve the multicast forwarding efficiency. To achieve this goal, the path to reach a multicast destination is biased towards those paths passing through another multicast destination. If multiple such choices are available, the one leading to the least extra cost is selected. Our protocol embeds this destination-driven feature into the on-demand multicast structure building process of an existing multicast protocol ODMRP. Detailed protocol design descriptions are provided. Simulation results show that our protocol can greatly improve the forwarding efficiency as compared with ODMRP. Moreover, our destination-driven design can also work well with other existing multicast routing protocols for wireless ad hoc networks.

Localized network coding is a promising technique to improve the throughput of wireless multihop networks with multiple concurrent unicast sessions. However, most existing mechanisms in this field perform network coding without considering the maximization of joint coding gain among neighboring nodes. In this paper, we study how to improve network performance by maximizing the area-centric coding gains in wireless networks. To achieve this goal, we design an efficient coding-aware transmission scheduling mechanism. Simulation results show that our mechanism can remarkably improve the network throughput as compared with existing mechanisms.

This paper presents a human-centered picture slideshow system for mobile users. In contrast to conventional ROIs (region-of-interest) detection based systems, we provide mobile users the freedom of personalizing ROIs in a convenient and effective way. Here, we import a simple human interaction, i.e., only a single click, to give a hint for users' ROIs. First, local saliency map (LSM) is generated, which considers not only multi-scale contrast, but also the self-correlation measure and central effect. Then a local fuzzy growing method is adopted to extract ROIs automatically based on LSM. Extensive experiments and user studies show the encouraging performance of the proposed system.

Considering sensor nodes deployed densely and uniformly in the sensing field, we focus on a scenario that a mobile sink moving through the sensing field queries a specific area or a point of interest for data collection. A Query packet is injected by the mobile sink and routed to the specific area, then the corresponding Response packet is returned to the mobile sink via multi-hop communication. Due to the mobility of the sink, the Query and Response should have different routes. We analyze such a network model to address the problem of efficient data collection in wireless sensor networks and propose an efficient Query-Based Data Collection Scheme (QBDCS). In order to minimize the energy consumption and packet delivery latency, QBDCS chooses the optimal time to send the Query packet and tailors the routing mechanism for partial sensor nodes forwarding packets. Simulation results demonstrate that QBDCS completes a query-based data collection cycle with minimum energy consumption and delivery latency.

An application scenario embracing the area of Intelligent Transportation Systems (ITS) is studied in this paper. The infrastructure of the application scenario is wireless sensor networks with mobile sinks. The WSN is query-based and with multi-hop transmission for data sensing. The application-specific network architecture was introduced and exploited. The detailed routing algorithm as well as its initialization phase were developed. Analysis was given on the end-to-end delivery latency, aiming to optimize the routing in both real-time ability and energy efficiency. Compared with the existing routing mechanism for the same application scenario, the proposed algorithm demonstrates less delivery latency and consequently lower energy consumption. Extensive simulations were conducted to evaluate the performance of the routing mechanism as well as to compare it with existing method numerically.

In this paper, meeting position aware routing (MPAR) is proposed for query-based mobile enabled wireless sensor network (mWSN). We consider mWSNs with dense sensor nodes deployed in the sensing field. A mobile sink (MS) moving through the sensing field with random speed queries a specific area or a point of interest for information from physical world. Queried data over a area is aggregated to a source node and delivered to the mobile sink via multi-hop communication. The feature of MPAR is that the mWSNs can predict the position of the mobile sink at the time it meets the data packet. The queried data packet is delivered towards this predicted position from the source node during the propagation. We compared MPAR with the existing routing protocol for the similar scenarios. Analysis was conducted to discover the conditions under which MPAR demonstrates the optimal performance. Extensive simulations were also conducted to evaluate the performance of the routing mechanism as well as to compare it with existing method. Evaluation results show that MPAR outperforms the existing mechanism in that it provides less end-to-end delivery latency, and consequently reduces the energy consumption. The robustness to variation of MS velocity is also improved.

In this paper, we analyze people’s reading and commenting behaviors in blogspace and proposed an algorithm for blog ranking. Upon two selected communities, AI and Medical, we show how comments, reading records, active browsing and multi time browsing can help to construct the weblog graph and reflect a blog’s popularity. Based on these analysis, we propose cRank, a graph based algorithm, to rank blog among community members. Finally, we divide our dataset temporally and present how the proposed algorithm can make prediction on blogs’ rankings. The experiment shows that cRank has a better performance upon several baseline systems.

Abstract In this paper, we design a localized power‐aware alternate routing (LPAR) protocol for dynamic wireless ad hoc networks. The design objective is to prolong the lifetime of wireless ad hoc networks wherein nodes can adaptively adjust their transmission power based on communication ranges. LPAR achieves this goalviatwo phases. In the first phase, energy draining balancing is achieved by identifying end‐to‐end paths with high residual energy. The second phase is designed to effectively reduce the power consumed for packet forwarding. This is achieved by iteratively performing adaptive localized power‐aware alternate rerouting to bypass each (potentially) high‐power link along the end‐to‐end path identified in the first phase. Further, the design of LPAR enables nodes to collect their neighborhood information ‘on‐demand’, which can effectively reduce the overhead for gathering such information. LPAR is suitable for both homogeneous and non‐homogeneous networks. Simulation results demonstrate that LPAR achieves improved performance in reducing protocol overhead and also in prolonging network lifetime as compared with existing work. Copyright © 2008 John Wiley & Sons, Ltd.

Sink's vicinity is a hotspot area in a wireless sensor network, and has a significant impact on the normal operation and network performance of the entire network. A hybrid MAC protocol makes use of the advantages of both CSMA/CA and TDMA protocols and adaptively switches between the two protocols based on dynamic traffic load, and can thus improve the network performance within the sink's vicinity. In this paper, we study MAC for the sink's vicinity in a WSN. We develop performance models for CSMA/CA and TDMA systems and compare their performance under both non-saturation and saturation conditions based on the developed performance models. Through simulation results, we verify that the accuracy and correctness of the performance models. Moreover, the analytical results based on the performance models can be used as a guide for determining the optimal load point for protocol switch and designing an optimal hybrid MAC protocol for the sink's vicinity.

Network coding has been considered as an effective strategy for improving the performance of wireless mesh networks (WMNs) by encoding multiple packets into a single transmission. Existing work shows that integration of network coding and routing at the network layer can achieve good performance in terms of network throughput and packet delay. In this paper, we propose a rate-adaptive coding-aware multiple path routing mechanism for WMNs. The main design objective is to improve the network performance via traffic splitting for maximizing the coding opportunities in the network. Simulation results are used to verify the effectiveness of our proposed mechanism.

Deploying multiple mobile sinks is an attractive approach to enhance the performance of wireless sensor networks. In this paper, we address the scenario of two mobile sinks. The two sinks can travel in the same region or in the divided regions separately. The problems are formulated by a lattice-based network model. We deduce the network lifetime and delay of data delivery in different mobility patterns. The relation between network performance and number of mobile sinks is also discussed. Simulation results are provided to validate our theoretic analysis followed by performance comparison of different mobility patterns.

Opportunistic routing and network coding have been considered as effective strategies for improving the throughput of wireless mesh networks (WMN). However, most existing work studied opportunistic routing and network coding separately. This has largely limited the ability of the above strategies from effectively improving the network performance. To achieve improved network throughput, in this paper, we propose a coding-aware opportunistic routing mechanism for WMNs. The design goal is achieved by effectively integrating the above two strategies such that decision on each packet forwarding is made with the awareness of potential coding opportunities. Simulation results show that our proposed mechanism can remarkably improve the network throughput.

Aiming at the periodical data gathering application of wireless sensor network, the transmission delay and the network traffic loads are analyzed for the route-based data aggregation algorithm. It is then investigated how to minimize the network traffic within the data delay bound. An optimization problem is deduced based on the traffic aggregation model. Inspired by the hill-climbing algorithm, a centralized algorithm named as ldquoEffectiveness based Aggregation Time Allocation (EATA)rdquo is introduced and evaluated. In our simulation experiments, EATA can lead the network to a maximal traffic aggregation performance and good adaptability compared with other existing algorithms.

Prolonging the network lifetime and reducing the delay of event delivery are both important in event-driven multi- hop wireless sensor networks. In this paper, an optimum predefined mobility trajectory of the sink was explored to balance the two performance metrics. We focus on a lattice-based analytical model for understanding performance as system and mobility parameters are scaled. Based on the model, the theoretical analysis and the simulation results validate our statements. The case of multiple mobile sinks are also discussed and simulated.

Aiming at the periodical data gathering application of Wireless Sensor Network, this paper first analyzed the network traffic flow and transmission delay in typical aggregation model. Then it investigated how to properly allocate aggregation time to minimize total network traffic within certain delay bound. An optimization problem was deduced and numerical solutions for regular network are calculated. Inspired by the numerical results, this paper introduced the concept of the critical aggregation level and presented an algorithm called Triangle-shaped Hierarchy aggregation Time Allocation (THTA). In our simulation experiments, THTA can lead the network to the maximum aggregation performance and fine adaptability compared with other existing algorithms.

Location-based routing has been a critical and efficient routing strategy in large wireless sensor networks (WSN) with mobile sinks. However, the performance of location-based routing highly depends on how position information of mobile sinks are managed and updated. This is typically the task of location service. In this paper, we present the design of a hierarchical location service for WSNs with mobile sinks. The main design objective is to greatly reduce the communication overhead for providing location service while maintaining high routing performance. Detailed simulation results are used to verify the high performance of our designed location service.

The combined and separated research advancement in the fields of sensing, communication, and computing have driven the research efforts in wireless sensor networks (WSN) in recent years. According to the traditional definition of WSNs, a dense, static, and prohibitively expensive sensor deployment is implicitly required, which has limited the feasibility of WSNs in non-military approaches. Aside from stationary sensor and sink deployment, the introduction of mobility of mobile terminals into WSNs can be leveraged to alleviate the burden of certain static sinks in order for efficient information repository, dissemination and sharing. In this paper, an architecture of multi-radio enabled mobile wireless sensor network (MEMOSEN) has been introduced, which has taken the heterogeneity and mobility of sensors and sinks into account. The capacity gain due to mobility in MEMOSEN has been addressed, and the design challenges and potential applications of MEMOSEN have been analyzed as well.

Introducing heterogeneous mobile devices, such as mobile phones into the large scale sparse wireless sensor networks is a promising research direction. These devices acting as mobile sinks offer many benefits to the network. For instance, they help to improve scalability, maintain load balance, conserve energy, prolong network lifetime and implement commercially. The paper investigates the impacts of different features and behavior of mobile sinks on the hybrid wireless sensor networks. Analysis and simulation results show that, instead of deploying mobile sinks as much as possible, choosing appropriate number, transmission range, velocity and gathering mode of the sink nodes can significantly decrease the average end-to-end data delivery delay and improve the energy conservation. The comparisons of performance metrics between fixed sinks and mobile sinks are also made in sparse networks along with the results that mobile sinks can bring higher data success rate and energy balance.

In this paper, a novel performance analytical model, which uses discrete Markov and M/M/1/K theory, is presented for IEEE 802.11 based wireless networks with finite load. Delay and loss caused by MAC contention and buffer queuing are studied in detail. This model and analysis give an in-depth insight and understanding to the unfairness between uplink and downlink flows that are forwarded by access point. Futhermore, based on this model, a resource allocation scheme for access point is proposed. This hybrid scheme uses bandwidth reservation based admission control and priority scheduling to guarantee bounded delay and loss for real-time traffics. Simulation results validate the analytical model and show that our scheme can avoid congestion and provide QoS under high traffic load

Multipath routing in mobile ad hoc networks has recently received more research efforts, where more than one route will be searched and maintained between every source-destination pair. Compared with traditional single-path routing, utilizing multiple paths in parallel would be of benefit to reduce end-to-end delivery delay as well as to split traffic for load balancing to achieve better utilization of bandwidth resource under moderate traffic load. Besides, multipath routing will enhance routing robustness in the sense of lengthening route lifetime. In this paper, we propose an analytical modeling framework to investigate multipath routing in ad hoc/multihop networks. The single-path model is considered as a multi-node M/M/1 tandem network with several congestion queues, and multiple paths can be viewed as several parallel rays originating from the focus of a convex lens. This proposed framework allows us to investigate issues such as optimal load distribution, end-to-end delivery delay and multipath routing reliability in ad hoc networks. Due to the flexibility, our modeling framework is applicable for heterogeneous network as well as homogeneous case

The future telecommunication systems will be surely characterized by the convergence of mobile technologies and Internet protocols, achieved through a network platform totally based on TCP/IP protocols ("all-IP network").In such a system, both wired and wireless parts are built on a common network platform based on TCP/IP protocol. However, TCP/IP protocol headers overheads are large and need to be compressed to save radio channel bandwidth which is the most expensive and scarce resource of the whole wireless system, especially for some services whose payload size is small. In this paper, a new adaptive robust TCP/IP header compression algorithm for 3G wireless networks is proposed and analyzed. The aim of this algorithm is to adjust the dimension of variable sliding window (VSW) in W-LSB encoding wiith the accurate estimation o wireless channel state to achieve the good balance of compression ratio and error-resistant robustness for the adaptive use in wireless link. We present simulation results that demonstrate the effectiveness of this adaptive algorithm over wireless link.

The queuing/scheduling algorithm is one of the most important mechanisms to provide guaranteed quality of service (QoS) in high speed packet-switched networks. By computing the system virtual time and per packet/connection virtual start/finish time, a number of packet fair queueing (PFQ) algorithms are designed to simulate GPS (generalized processor sharing). The difference in computation complexity is due to the variable ways to compute system time. Many algorithms, including WFQ/WF/sup 2/Q, SCFQ, SPFQ, WF/sup 2/Q+, VC, etc., have been proposed to use different system virtual time (also known as system potential) function. This paper proves that all the latency-rate (LR) servers only need to calculate their system virtual times once per packet service time, no matter how many packet arrivals occur in this interval. Thus, it is a general scheme that benefits all the well-known LR PFQ algorithms.

Being a part of IEEE project 802, the 802.11 medium access control (MAC) is used to support asynchronous and time bounded delivery of radio data packets. It is proposed that a distributed coordination function (DCF), which uses carrier sense multiple access with collision avoidance (CSMA/CA) and binary slotted exponential backoff, be the basis of the IEEE 802.11 WLAN MAC protocols. This paper proposes a throughput enhancement mechanism for DCF by adjusting the contention window (CW) resetting scheme. Moreover, an analytical model based on Markov chain is introduced to compute the enhanced throughput of 802.11 DCF. The accuracy of the model and the enhancement of the proposed scheme are verified by elaborate simulations.

IEEE 802.11 medium access control (MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is a distributed coordination function (DCF), which is a carrier sense multiple access with collision avoidance (CSMA/CA) with a binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Compared with other models, this model is shown to be able to predict the behavior of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

IETF proposes the differentiated services (DiffServ) architecture to implement QoS in the Internet. However, recent studies have shown that under various conditions, the existing DiffServ mechanism may have problems of unfairness and inefficient resource utilization, thereby failing to achieve the desired QoS for TCP flows running over assured services. This paper considers that the idea of TCP friendly congestion control should be used in traffic conditioning (TC) for AF PHB (assured forwarding per-hop behavior), especially when the customer expects to achieve better performance by out-of-profile traffic. This paper also proposes using a direct congestion control scheme (DCCS) and dynamic traffic conditioning (DTC) to achieve fairness between responsive and unresponsive aggregates as well as better allocations for responsive flows (TCP) with different packet lengths, micro-flow number, round trip time (RTT) etc. Moreover, a better resource utilization can be achieved at the same time.

Precision and efficiency are the two most important goals of simulation. As far as communication networks are concerned, for the great complexity of their topologies, protocols and traffic, how to simulate them efficiently has been a hot spot in the recent research. This article first emphasizes the importance of the survey before the simulation begins, because it can make the target of simulation clear. Also, during the modeling phase prior to simulation, we should consider adequately the characteristics of the communication network to be simulated and adopt some methods in order to improve the simulation efficiency.