Public vehicle service (PVS), as a paradigm to manage and share large-capacity vehicles for public passenger delivery, is promising to improve the quality of urban transportation. In the PVS system, a command center receives requests sent by passengers, periodically assigns them to public vehicles and schedules vehicle routes to serve the requests. However, in the PVS system, the passengers' waiting time is not well utilized. Moreover, it is observed that the driving distance on low-speed roads accounts for a rather high percentage. These two factors impact on system efficiency. In order to utilize the waiting time, we propose to let passengers walk a short distance instead of standing at their origins. At the same time, the driving distance on low-speed roads will be reduced. In this paper, the closest meeting point algorithm is proposed to address the challenge of determining the best pick-up and drop-off locations. The large-scale simulations show that the passenger walking and the proposed fast-route scheduling strategy can shorten the total vehicle travel distance by 34%.

Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.

ZigBee is a widely used wireless technology in low-power and short-range scenarios such as the Internet of Things, sensor networks, and industrial wireless networks. However, the traditional ZigBee supports only one data rate, 250 Kbps, which thoroughly limits ZigBee's efficiency in dynamic wireless channels. In this paper, we propose Mrs. Z, a novel physical layer design to enable multi-rate selection in ZigBee with lightweight modification on the legacy ZigBee modules. The key idea is to change the single spectrum spreading length to multiple ones. Correspondingly, to support the rate adaptation to the channel variations, we propose a bit-error-based rate selection scheme, which predicts BER by leveraging the physical properties of ZigBee to calculate the confidence for each symbol in transmission. Then, the receiver selects the rate based on the negative impact on throughput incurred by bit errors and gives feedback to the transceiver. We implement Mrs. Z on USRPs and evaluate its performance in different scenarios. Experiment results demonstrate that Mrs. Z achieves about 1.15, 1.2, and 1.8 × average throughput compared to the classic smart pilot, softrate, and the traditional ZigBee.

Flow monitoring is widely applied in softwaredefined networks for monitoring network performance. Especially, the detection on heavy hitters can prevent the Distributed Denial of Service attack. However, many existing approaches fall in one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) low accuracy caused by the sacrifice with fast detection. As a result, we aim to find a balance between the accuracy and efficiency of flow monitoring, where the network resources can be saved and the error rate can also be confined simultaneously. In this paper, we present ReFeR, a novel “Report-FeedbackReport” scheme to improve the detection efficiency of heavy item detecting while ensuring low error rate of the measurement. ReFeR leverages the binary order of magnitude of item measurement to replace the long statistical information shared between switches and controller; after the items are analyzed with the magnitude, only fewer uncertain items are involved in further detection, where their information (i.e., significant digits) is provided for final judgment. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. ReFeR keeps the error rate under 1% and the saving rate larger than 20% in most cases as the selecting fraction α> 524, which guarantees both high efficiency and low error rate compared with existing methods.

Web 2.0 emphasizes user-generated content, usability, and interoperability for end users. As a killer application in web 2.0, Folksonomy is a collaborative tagging system and enables users to annotate Web objects freely and conveniently. In this paper, we propose an approach to build and enrich profiles of Internet objects (users and resources) based on the relations provided by Folksonomy among tags, resources, and users. This approach extracts semantic relations among tags and constructs a tag hierarchy for calculating similarities of Internet objects, so as to let Internet service or content providers perceive meaning of tags as human beings do. We examine the performance of our approach via cross validation on real datasets and show that, with profile enrichment, recommendation performance measure F1 is increased by 10.0417% compared to recommendation without profile enrichment.

ZigBee is a widely used wireless technology in low-power and short-range scenarios such as Internet of Things (IoT), sensor networks, and industrial wireless networks. However, the standard ZigBee supports only one data rate, 250Kbps, which thoroughly limits ZigBee's efficiency in dynamic wireless channels. In this paper, we propose Mrs.Z, a novel physical layer design to enable multi-rate selection in ZigBee. The key idea is to change the single spectrum spreading length to multiple ones. Correspondingly, to gracefully adapt to the channel variations, we propose a BER-based rate selection scheme, dividing bit errors into two categories: errors caused by the exceeding despreading threshold, which can be discovered in the physical layer, and caused by incorrect despreading, which is not visible until cyclic redundancy check (CRC) in the media access control (MAC) layer. Then, the receiver selects the rate based on the underlying negative impacts incurred by them and feedbacks to the transceiver. We implement Mrs.Z on USRPs and evaluate its performance in different scenarios. Results demonstrate that Mrs.Z achieves an improvement of 20% and 80% compared to the classic SoftRate and the standard ZigBee.

In this paper, we consider tensor completion under adaptive sampling of tensor (a multidimensional array) fibers. Tensor fibers or tubes are vectors obtained by fixing all but one index of the array. This sampling is in contrast to the cases considered so far where one performs an adaptive element-wise sampling. In this context we exploit a recently proposed algebraic framework to model tensor data [1] and model the underlying data as a tensor with low tensor tubal-rank. Under this model we then present an algorithm for adaptive sampling and recovery, which is shown to be nearly optimal in terms of sampling complexity. We apply this algorithm for robust estimation of RF fingerprints for accurate indoor localization. We show the performance on real and synthetic data sets. Compared to existing methods, that are primarily based on non-adaptive matrix completion methods, adaptive tensor completion achieves significantly better performance.

A well-designed energy-efficient routing protocol is an indispensable part for prolonging the lifetime of wireless sensor networks (WSNs) because a sensor node usually has limited energy. Many research efforts are contributed on routing design in WSNs. With the development of green technology, the energy harvesting technique is being applied to real WSNs. Therefore, existing routing protocols are not suitable for such new WSNs with energy harvesting. In this paper, we concentrate on designing a novel routing protocol, named energy harvesting routing (EHR), which takes energy harvesting as one major factor into routing design to improve the energy efficiency. First, we introduce a hybrid routing metric combining the effect of residual energy and energy harvesting rate. Then we propose an updating mechanism allowing every node to maintain dynamic energy information of its neighbors. Based on the hybrid metric and the neighbor information, EHR is able to locally select the optimal next hop. Extensive simulations are conducted to evaluate the performance of EHR. Results demonstrate that EHR outperforms existing routing protocols in energy harvesting WSNs in term of the energy efficiency.

Public vehicle (PV) systems will be efficient traffic-management platforms in future smart cities, where PVs provide ridesharing trips with balanced QoS (quality of service). PV systems differ from traditional ridesharing due to that the paths and scheduling tasks are calculated by a server according to passengers' requests, and all PVs corporate with each other to achieve higher transportation efficiency. Path planning is the primary problem. The current path planning strategies become inefficient especially for traffic big data in cities of large population and urban area. To ensure real-time scheduling, we propose one efficient path planning strategy with balanced QoS (e.g., waiting time, detour) by restricting search area for each PV, so that a large number of computation is saved. Simulation results based on the Shanghai (China) urban road network show that, the computation can be reduced by 34% compared with the exhaustive search method since many requests violating QoS are excluded.

Mobile crowd sensing (MCS) has become a promising paradigm to perceive the environment with the help of smart phones. A monetary award is an effective method to incentivize participants to contribute good quality data. However, the reward for long-term data collection in the wide area could be unaffordable for a MCS requester. In this paper, we enable data requesters to recruit sensing workers in a group buying way. Requesters with similar data demand can form a group to share the payment and sensing data, which reduces cost and increases the coverage of sensing data. Agents from different groups compete in buying sensing data from sensing workers. We propose TGBA, a Two-phase Group Buying based Auction mechanism for MCS. In phase I, requesters submit bids to their group agent, the group agent decides the winners and clearing prices. In phase II, group agents attend the auction for recruiting workers. TGBA is computationally efficient and possesses good economic properties such as individual rationality, budget balance and truthfulness.

In Intelligent Transport field, the Public Vehicle System is proposed to introduce a concept of a specialized vehicle for public transportation, which can integrate and substitute for vehicles such as taxis, buses and railways. Public Vehicle model has several advantages over earlier models, but the algorithm proposed in the model can't consider all potential solutions when building new paths, resulting in a decrease in performance. We expand the searching method to cover all cases of insertion and achieve a lower cost. Our simulations show that the Combinatorial Insertion Algorithm can have a 5%-11% promotion in total traveling distance.

This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promising approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint databasea priorifor localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as atensorand use tensor algebraic methods for anadaptivetubal-sampling of this fingerprint space. In particular, using a recently proposed tensor algebraic framework in[1], we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation, the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions, we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by$71$percent for the high SNR case and$55$percent for the low SNR case.

The increasing number of vehicles nowadays makes it hard to manage traffic flow on the streets, especially when it comes to intersection management. This paper proposes the use of dashboard traffic lights (DTL), an intelligent traffic light system based on client-server communication that will use V2I (Vehicle-to- Intersection) network scheme to send request messages from moving vehicles to the intersection station that will analyze the request message and send back the decision message based on the intersection status. The average waiting time and the number of vehicles stopping at the intersection is significantly reduced.

The proliferation of smartphones has enabled a novel paradigm, participatory sensing, which leverages the smartphones to collect and share data about their surrounding environment. Since the sensing tasks are location-dependent and have time features, it is crucial and challenging to find a proper allocation of sensing tasks to ensure the timeliness of tasks and the quality of sensing data. In this paper, we investigate the heterogeneous sensing task allocation problem aiming at minimizing the total penalty caused by the tardiness of tasks. We prove this problem is NP-hard and propose two hybrid algorithms which combine a heuristic algorithm and two meta-heuristic algorithms respectively. The extensive simulation results show that the proposed hybrid algorithms outperform the meta-heuristic algorithms.

Substantial technology advances have been made in areas of autonomous and connected vehicles, which opens a wide landscape for future transportation systems. We propose a new type of transportation system, Public Vehicle (PV) system, to provide effective, comfortable, and convenient service. The PV system is to improve the efficiency of current transportation systems, \eg, taxi system. Meanwhile, the design of such a system targets on significant reduction in energy consumption, traffic congestion, and provides solutions with affordable cost. The key issue of implementing an effective PV system is to design efficient scheduling algorithms. We formulate it as the PV Path (PVP) problem, and prove it is NP-Complete. Then we introduce a real time approach, which is based on solutions of the Traveling Salesman Problem (TSP) and it can serve people efficiently with lower costs. Our results show that to achieve the same performance (e.g., the total time: waiting and travel time), the number of vehicles can be reduced by 47%-69%, compared with taxis. The number of vehicles on roads is reduced, thus traffic congestion is relieved.

Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of9,000 m even when up to 50% original data are missing.

Fingerprinting based localization is one of the most widely used indoor localization methods. This method is divided into two phases: during the off-line training phase, fingerprints within the area of interest are collected and stored in a fingerprint database; during the on-line mapping phase, the real-time location of a device is estimated by mapping itself to the most accurate fingerprint in the database. The efficiency of the mapping process is one of the key challenges of the on-line phase, and is mostly characterized by the localization accuracy and the response time. Clustering methods have been introduced to reduce computational overhead. In this paper, we propose a dynamic clustering method leveraging the inertial information of the target device. An active area is dynamically computed around the prior position. The target mapping space is significantly reduced with this active area. This method can be integrated with other clustering algorithms to overcome the edge problem and remove outliers. We evaluate this method compared with the state-of-the-art methods on a body sensor based localization system. The results show that the accuracy, precision and response time of the system are improved greatly.

The increasing number of vehicles nowadays makes it hard to manage traffic flow on the streets, especially when it comes to intersection management. This paper proposes the use of dashboard traffic lights (DTL), an intelligent traffic light system based on client-server communication that will give each vehicle a decision based on the speed and direction of the vehicle. This system can be used for both human controlled vehicles and autonomous cars. The average waiting time for all vehicles and the number of cars stopping at the intersection is significantly reduced. The intersection throughput is increased.

Environment reconstruction is to rebuild the physical environment in the cyberspace using the sensory data collected by sensor networks, which is a fundamental method for human to understand the physical world in depth. A lot of basic scientific work such as nature discovery and organic evolution heavily relies on the environment reconstruction. However, gathering large amount of environmental data costs huge energy and storage space. The shortage of energy and storage resources has become a major problem in sensor networks for environment reconstruction applications. Motivated by exploiting the inherent feature of environmental data, in this paper, we design a novel data gathering protocol based on compressive sensing theory and time series analysis to further improve the resource efficiency. This protocol adapts the duty cycle and sensing probability of every sensor node according to the dynamic environment, which cannot only guarantee the reconstruction accuracy, but also save energy and storage resources. We implement the proposed protocol on a 51-node testbed and conduct the simulations based on three real datasets from Intel Indoor, GreenOrbs and Ocean Sense projects. Both the experiment and simulation performances demonstrate that our method significantly outperforms the conventional methods in terms of resource efficiency and reconstruction accuracy.

A modern general-purpose graphics processing unit (GPGPU) usually consists of multiple streaming multiprocessors (SMs), each having a pipeline that incorporates a group of threads executing a common instruction flow. Although SMs are designed to work independently, we observe that they tend to exhibit very similar behavior for many workloads. If multiple SMs can be grouped and work in the lock-step manner, it is possible to save energy by sharing the front-end units among multiple SMs, including the instruction fetch, decode, and schedule components. However, such sharing brings architectural challenges and sometime causes performance degradation. In this article, we show our design, implementation, and evaluation for such an architecture, which we call Buddy SM . Specifically, multiple SMs can be opportunistically grouped into a buddy cluster. One SM becomes the master, and the rest become the slaves. The front-end unit of the master works actively for itself as well as for the slaves, whereas the front-end logics of the slaves are power gated. For efficient flow control and program correctness, the proposed architecture can identify unfavorable conditions and ungroup the buddy cluster when necessary. We analyze various techniques to improve the performance and energy efficiency of Buddy SM. Detailed experiments manifest that 37.2% front-end and 7.5% total GPU energy reduction can be achieved.

Data collection is a crucial operation in wireless sensor networks. The design of data collection schemes is challenging due to the limited energy supply and the hot spot problem. Leveraging empirical observations that sensory data possess strong spatiotemporal compressibility, this paper proposes a novel compressive data collection scheme for wireless sensor networks. We adopt a power-law decaying data model verified by real data sets and then propose a random projection-based estimation algorithm for this data model. Our scheme requires fewer compressed measurements, thus greatly reduces the energy consumption. It allows simple routing strategy without much computation and control overheads, which leads to strong robustness in practical applications. Analytically, we prove that it achieves the optimal estimation error bound. Evaluations on real data sets (from the GreenOrbs, IntelLab and NBDC-CTD projects) show that compared with existing approaches, this new scheme prolongs the network lifetime by 1.5X to 2X for estimation error 5-20 percent.

Nowadays, RFID systems have been widely deployed for applications such as supply chain management and inventory control. One of their most essential operations is to swiftly identify individual tags to distinguish their associated objects. Most existing solutions identify tags sequentially in the temporal dimension to avoid signal collisions, whose performance degrades significantly as the system scale increases. In this paper, we propose aParallel Identification Protocol(PIP) for RFID systems, which achieves the parallel identification paradigm and is compatible with current RFID devices. Uniquely, PIP encodes the tag ID into a specially designed pattern and thus greatly facilitates the reader to correctly and effectively recover them from collisions. Furthermore, we analytically investigate its performance and provide guidance on determining its optimal settings. Extensive simulations show that PIP reduces the identification delay by about 25%-50% when compared with the standard method in EPC C1G2 and the state-of-the-art solutions.

The data gathering is a fundamental operation in wireless sensor networks. Among approaches of the data gathering, the compressive data gathering (CDG) is an effective solution, which exploits the spatiotemporal correlation of raw sensory data. However, in the multi-attribute scenario, the performance of CDG decreases in every attribute's capacity because more measurements are on demand. In this paper, under the general framework of CDG, we propose a multi-attribute compressive data gathering protocol, taking into account the observed interattribute correlation in the multi-attribute scenario. Firstly, we find that 1) the rapid growth of the demand on measurements may decline the network capacity, 2) according to the compressive sensing theory, correlations among attributes can be utilized to reduce the demand on measurements without the loss of accuracy, and 3) such correlations can be found on real data sets. Secondly, motivated by these observations, we propose our approach to decline measurements. Finally, the real-trace simulation shows that our approach outperforms the original CDG under multiattribute scenario. Compared to the CDG, our approach can save 16% demand on measurements.

Electric vehicle (EV) is a promising transportation with plenty of advantages, e.g., low carbon emission, high energy efficiency. However, it requires frequent and long time charging. In public charging stations, EVs spend long time on queuing especially during peak hours. Hence, it requires an efficient method to reduce the total charging time for EVs. We study the Electric Vehicle Charging-Scheduling (EVCS) problem in this paper. First we prove that EVCS is NP-Complete, which can be reduced from one Parallel Machine Scheduling (PMS) problem. Then two heuristic algorithms are proposed: the Earliest Start Time (EST) algorithm, and the Earliest Finish Time (EFT) algorithm. EST tries to advance the start charging time to get customers in service as early as possible, while EFT focuses on the possible finish charging time to get customers served as soon as possible. Finally simulations show that, the proposed algorithms outperform the classic greedy nearest scheduling algorithm: assign each EV to its nearest charging station, then choose the outlet where the fewest EVs are queuing. Typically, under our simulation settings, the average finish time and maximum finish time can be reduced by about one hour, and six hours respectively.

Reconstructing the environment by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the reconstruction accuracy. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when the missing data rate becomes large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of low-rank structure, spatial similarity, temporal stability and multi-attribute correlation. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTI-CS) algorithm with a multi-attribute assistant (MAA) component for data reconstruction. Finally, extensive simulation results on real sensory datasets show that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy.

Coverage is a fundamental problem in wireless sensor networks (WSNs). Conventional studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a field of interest (FoI) is complex in many real-world applications. However, existing coverage studies do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the field of interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. Thus, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, what is the expected coverage ratio when a number of sensors are adopted? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct extensive simulations to evaluate the performance of the proposed algorithms.

In wireless sensor networks (WSNs), since many basic scientific works heavily rely on the complete sensory data, data recovery is an indispensable operation against the data loss. Several works have studied the missing value problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a multiple attributes-based recovery algorithm which can provide high accuracy. Firstly, based on two real datasets, the Intel Indoor project and the GreenOrbs project, we reveal that such correlations are strong, e.g., the change of temperature and light illumination usually has strong correlation. Secondly, motivated by this observation, we develop a Multi-Attribute-assistant Compressive-Sensing-based (MACS) algorithm to optimize the recovery accuracy. Finally, real trace-driven simulation is performed. The results show that MACS outperforms the existing solutions. Typically, MACS can recover all data with less than 5% error when the loss rate is less than 60%. Even when losing 85% data, all missing data can be estimated by MACS with less than 10% error.

Recent advances in wireless communication and electronic manufacture have enabled a variety of sensors to be used for Wireless Body Area Networks (WBANs), which can provide real-time body monitoring and feedback for enabling patient diagnostics procedure, rehabilitation, sports training and interactive performance. However, existing single-hop wireless communication scheme faces several major challenges: rapid growth of channel conflicts as more sensors added, impermeability of human body to radio waves and highly dynamic network topology due to human movements. In this paper, a prototype of multi-hop WBAN has been built to quantify the channel conflict and to characterise the network connectivity during human motions. A probability based routing protocol fusing inertial sensor data and history link quality is then developed, which aims at capturing the high spatio-temporal change of network topology on the selection of a reliable relay node in WBAN routing. The performance of the protocol is experimentally evaluated on our prototype system. Compared with a number of existing routings, the proposed scheme is more splendid in terms of average delivery ratio, number of hops and end-to-end delay.

Reconstructing the environment in cyber space by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the accuracy of reconstruction. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when missing data become large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of spatial correlation, temporal stability and low-rank structure. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTICS) algorithm to optimize the missing data estimation. Finally, the extensive experiments with real-world sensory data shows that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy. Typically, ESTICS can successfully reconstruct the environment with less than 20% error in face of 90% missing data.

An urban vehicular network is a typical type of Delay Tolerant Network (DTN). Based on the routing analysis in a DTN, we first put forward a Minimum Delay and Hop Algorithm (MDHA), which requires both historical and future information on all the vehicles in the network. Since MDHA is not practical, we then design a Traffic Aware Routing Algorithm (TARA), which uses the historical and the real-time vehicle information to make routing decisions on the road structure level. A simulation using real GPS data in Shanghai shows that TARA significantly reduces the transmission delay and the hop count compared to the traditional GEO routing and GPSR.

This paper introduces WiBEST, Wireless Body and Environmental Sensor Tracking platform, for personal indoor positioning. WiBEST is built with portable on-body sensor nodes and assisted sensor nodes deployed in the targeted indoor area. It takes a hybrid approach with pedestrian dead reckoning and radio-based localization and explore the their cooperative efforts. Real-time inertial measurements are combined with RSSI-based information, and then processed with an Extended Kalman Filter to be weighted in the location estimation according to their reliability. WiBEST also incorporates with an adaptive Step Length Algorithm to reduce the deviation of the measurements.The experimentation results show that WiBEST can improve the accuracy of the positioning by 66.3% compared to pure inertial solution. With the popularity of wearable devices with inertial sensors and wireless communication chips, we believe that this approach is very promising for personal indoor positioning services.

This paper studies delay-bounded service discovery in urban Vehicular Ad-hoc Networks (VANETs), which refers to locating resources and services (e.g., local sensor data and multimedia content) distributed on individual vehicles in the network within a certain delay bound. To facilitate the discovery process, a set of vehicles, called service directories (SDs), can be selected to store the index information of all the resources in the network. Selecting an optimal SD set with minimal size while satisfying the users' requirement of a bounded query response delay is very difficult due to the disruptive nature of VANETs. In this paper, we formulate the Delay Bounded Service Directory Selection (DB-Sel) problem as an optimization problem that minimizes the number of SDs under the delay bound constraint. We prove theoretically that the DB-Sel problem is NP-Complete even when the future positions of vehicles are known a priori. We observe and prove that the number of vehicles encountered by arbitrarily selected SDs within a given delay follows a normal distribution. We also find the contact probabilities among the vehicles exhibit strong temporal correlation. With these observations, we develop a heuristic algorithm which iteratively selects the best candidate according to the normal distribution property and the historical contact probability. We prove that our algorithms have a guaranteed performance approximation ratio compared to the optimal solution. Extensive trace-driven simulation results demonstrate that our algorithm can guarantee the required query delay and select SD sets 20% smaller than those selected by alternative algorithms.

Data loss is ubiquitous in wireless sensor networks (WSNs) mainly due to the unreliable wireless transmission, which results in incomplete sensory data sets. However, the completeness of a data set directly determines its availability and usefulness. Thus, sensory data recovery is an indispensable operation against the data loss problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a novel sensory data recovery algorithm which exploits the spatial and temporal joint-sparse feature. Firstly, by mining two real datasets, namely the Intel Indoor project and the GreenOrbs project, we find that: (1) for one attribute, sensory readings at nearby nodes exhibit inter-node correlation; (2) for two attributes, sensory readings at the same node exhibit inter-attribute correlation; (3) these inter-node and inter-attribute correlations can be modeled as the spatial and temporal joint-sparse features, respectively. Secondly, motivated by these observations, we propose two Joint-Sparse Sensory Data Recovery (JSSDR) algorithms to promote the recovery accuracy. Finally, real data-based simulations show that JSSDR outperforms existing solutions. Typically, when the loss rate is less than 65%, JSSDR can estimate missing values with less than 10% error. And when the loss rate reaches as high as 80%, the missing values can be estimated by JSSDR with less than 20% error.

With recent progress in vehicle autonomous driving and vehicular communication technologies, vehicle systems are developing towards fully connected and fully autonomous systems. This paper studies lane assignment strategies for connected autonomous vehicles in a highway scenario and their impact on the overall traffic efficiency and safety. We formulate a model of connected autonomous vehicles, which includes three features: traffic data available online, ultra-short reaction time, and cooperative driving. Based on this model, we propose a novel lane change maneuver Politely Change Lane (PCL), which achieves the tradeoff between traffic safety and efficiency. Its effectiveness is validated and evaluated by extensive simulations. The performance shows that PCL improves both safety and efficiency of the overall traffic, especially with heavy traffic.

Directional antennas provide great performance improvement for wireless networks, such as increased network capacity and reduced energy consumption. Nonetheless new media access and routing protocols are required to control the directional antenna system. One of the most important protocols is neighbor discovery, which is aiming at setting up links between nodes and their neighbors. In the past few years, a number of algorithms have been proposed for neighbor discovery with directional antennas. However, most of them cannot work efficiently when taking into account the collision case that more than one node exist in one directional beam. For practical considerations, we propose a new neighbor discovery algorithm to overcome this shortcoming. Moreover, we present a novel and practical mathematical model to analyze the performance of neighbor discovery algorithms considering collision effects. Numerical results clearly show our new algorithm always requires less time to discover the whole neighbors than previous ones. To the best of our knowledge, it is the first complete, practical analytical model that incorporates directional neighbor discovery algorithms.

In most delay tolerant network (DTN) applications, mobile nodes utilize WiFi radios to obtain local information and transmit data. One bottleneck on DTN delivery performance is the short communication range of the WiFi radio. Rather than designing efficient protocols on WiFi based DTN, we propose a novel dual-radio architecture by adding a long-range low-bitrate eagle eye (EE) radio on every node. This EE radio can “see” real-time movement information of nodes in a significantly large range, and so much early scheduling can be done with this radio when compared with WiFi which is still in charge of data transmissions. Benefiting from this cooperative dual radios architecture, we design distributed EE routing protocol for minimizing delivery delay in DTNs. Through our prototype implementation with 7 EE devices and simulations based on real trace data of 4000 taxis in Shanghai, we show that the proposed architecture is able to achieve as low as 40% of the average delay with traditional DTNs.

This paper studies mobile barrier coverage (MBC) surrounding dynamic objects. In the real world, several dynamic objects can benefit from MBC. For example, marching troop can detect any adversary intrusion without blind spot by MBC. However, conventional works only focused on barrier coverage for static objects, which fail when the objects start to move. Issues to address these dynamic-object scenarios, we propose the problem of mobile barrier coverage for dynamic objects. The most challenge is how to effectively maintain MBC when the motion of objects are unpredictable. We propose a fully distributed algorithm for mobile sensor nodes to cooperatively move and maintain the high-quality barrier coverage. The extensive simulations based on large-scale trace data demonstrate the efficiency and efficacy of the proposed algorithm.

This paper proposes techniques for face detection and gives the implementation details for an FPGA development board. We analyze and discuss the relation between the system computation cost and selection of the image scaling factor. We give a new method to select the stop threshold for the image reduction process, which reduces the total computation by half. We also provide a color image output mode to let our system enjoy more human-oriented design. Test results show that the system achieves real-time face detection speed (100 fps) and a high face detection rate (87.2%) for an SVGA (600 × 800) video source. The low power consumption (3.5W) is another advantage over previous work.

In surveillance related Wireless Sensor Networks (WSNs) applications, mobile targets can utilize intelligent motion planning in robotics to avoid detection, which could impose great threats on the sensor field. In this paper, we propose countermeasures against mobile target motion planning. The proposed solution enables the sensors to interfere the localization and map generation on mobile targets so as to expose them in the sensor field. We compare the behaviors of a mobile target in naive mode, smart mode with Simultaneous Location And Mapping (SLAM) and interfered mode where sensor nodes use different puzzle algorithms to disturb it. The simulation results show that the puzzle algorithms can interfere SLAM on mobile targets and enhance the target detection performance in a WSN. We also build an experimental testbed with Iris motes and a mobile robot to validate our solution.

Multi-Packet Reception (MPR) enables simultaneous receptions from different transmitters to a single receiver, which has been demonstrated to bring capacity improvement in wireless network. However, MPR does not improve the transmission capability of intermediate relay nodes in a multi-hop routing and thus these nodes may become the bottlenecks for increasing throughput despite of great reception capability. We investigate the scheduling for multi-hop routing with MPR to improve the network throughput under multiple data flows. We formulate the optimization problem under K-MPR model and analyze the performance upper bound with ideal scheduling. We propose a distributed scheduling scheme based on a k-Connected k-Dominating Set backbone to eliminate bottleneck effects on intermediate relay nodes as to enhance the network throughput. We show the effectiveness of our scheme by comparing its performance with the upper bound and node-disjoint routing.

In a target detection application, rational adversary targets that are conscious of the deployed location of sensor nodes are capable of planning a path in order to avoid being detected by sensor nodes. Probing sensor communication is one of the means that are used by adversary targets to get the necessary information. Therefore, this paper investigates how to reduce the sensor communication exposure. We introduce target bypassing routing methods applied on omnidirectional and directional sensor communication models to achieve the goal of reducing sensor communication exposure to targets. The simulation results show that our bypassing methods can decrease the possibility of communication exposure to a large extent. We also implement a prototype system with Iris motes to verify our solution.

Multicast benefits data center group communication in both saving network traffic and improving application throughput. The SLA (Service Level Agreement) of cloud service requires the computation correctness of distributed applications, translating to the requirement of reliable Multicast delivery. In this paper we present RDCM, a novel reliable Multicast approach for data center network. The key idea of RDCM is to minimize the impact of packet loss on the Multicast performance, by leveraging the rich link resource in data centers. A Multicast-tree-aware backup overlay is purposely built on group members for peer-to-peer packet repair. Riding on Unicast, packet repair not only achieves complete repair isolation, but also has high probability to bypass the pathological links in the Multicast tree where packet loss occurs. The backup overlay is organized in such a way that it causes little individual repair burden, control overhead, as well as overall repair traffic. We have implemented RDCM as a user-level library on Windows platform. The experiments on our test bed show that RDCM handles packet loss without obvious throughput degradation during high-speed data transmission.

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. We address the throughput optimization problem in wireless networks that support multipacket reception (MPR) capability. The problem is modeled as a joint routing and scheduling problem, which is known to be NP-hard. The scheduling subproblem deals with finding the optimal schedulable sets, which are defined as subsets of links that can be scheduled or activated simultaneously. We demonstrate that any solution of the scheduling subproblem can be built with \vert E\vert + 1 or fewer schedulable sets, where \vert E\vert is the number of links of the network. This result is in contrast with previous works that stated that a solution of the scheduling subproblem is composed of an exponential number of schedulable sets. Due to the hardness of the problem, we propose a polynomial time scheme based on a combination of linear programming and approximation algorithm paradigms. We illustrate the use of the scheme to study the impact of design parameters on the performance of MPR-capable networks, including the number of transmit interfaces, the beamwidth, and the receiver range of the antennas.

We present a multi-objective optimization approach for joint throughput optimization and traffic engineering, where the routing request of traffic arrives one-by-one. We provide an Integer Linear Program (ILP) that simultaneously i) maximizes the aggregate throughput, ii) minimizes the resource consumption, and iii) minimizes the maximum link utilization. We study the impact of optimizing the three different objectives simultaneously in dynamic environments, and show that better solutions than those of mono-objective approaches can be obtained. Because of the complexity of the ILP, we also propose another ILP with reduced complexity, and study its performance and the optimality gap between it and optimal solutions.

In wireless sensor networks, the nodes near the sink suffer from heavy traffic load. The recent progresses in physical layer make multi-packet reception (MPR) feasible, which may alleviate the suffering nodes. In this paper, we utilize MPR to improve the throughput of wireless sensor networks while maintain the fairness requirement for each sensor. Firstly, we formulate an optimization problem for wireless sensor networks using MPR. The result of the optimization is an upper bound for the network throughput. Secondly, we give a feasible heuristic scheme. In the performance evaluation, we analyse the throughput gap between the upper bound and the performance of the heuristic scheme. We also study the performance of the heuristic scheme by adjusting several network parameters and the results show that MPR brings notable throughput gain in wireless sensor networks and give some network design implications.

Sink mobility has attracted much research interests in Wireless Sensor Networks (WSNs), because it could provide energy saving and reduce latency during data collection. However, the mobile sink node is still a single point of failure in many WSNs applications, thus needs to be particularly protected against adversaries. We propose in this paper a moving strategy for the mobile sink which prevents tracking or detecting on it by adversaries during its data collection phase around the sensor field. Our moving strategy aims to selecting a trajectory for mobile sink node, which minimizes the total number of message communication from all static sensor nodes to the mobile sink node (including multi-hop relaying) and thereby reducing the possibility of being detected by the adversaries. We also employed a routing protocol on sensor nodes to forward the data to mobile sink node with shortest-path. Four strategies are evaluated in our simulation and the performance results show that our moving strategy we proposed achieves best goal and adapts well to the different deployment patterns.

In this article, we focus on a typical application of Cyber Physical System (CPS), i.e., vehicle route navigation. Two fundamental problems have been examined: 1) How different are various vehicle routing algorithms? 2) How valuable is real-time traffic information or historical traffic information in helping vehicle routing? Different from most previous works based on simulations, we presented performance comparisons of four routing algorithms using real GPS sensory data from 4000 taxis. It has been shown that using real-time traffic information could substantially improve the quality of vehicle path routing. This can benefit an individual driver, because through our evaluation results on realistic vehicle traces we found that the paths selected by taxi drivers are not as good as expected. More importantly, utilizing real-time information could improve global transportation efficiency in terms of dispersing/managing traffic, which plays a key role in constructing an effective vehicular CPS.

Throughput optimization and traffic engineering in Wavelength-Division Multiplexing (WDM) networks are usually treated as mono-objective optimization problems. In this paper, we provide a multi-objective Integer Linear Program (ILP) for the joint throughput optimization and traffic engineering problem. By simultaneously i) maximizing the throughput, ii) minimizing the resource consumption, and iii) balancing the traffic load, we demonstrate that better solutions than those of mono-objective approaches are obtained. We also present a distributed heuristic algorithm, which upper-bounds the per-route resource consumption and maximizes the throughput. Simulation results validate the proposed model and heuristic algorithm. Additionally, we present an ILP formulation for another well-known problem such as routing and wavelength assignment (RWA), and discuss the impact of modeling it as a multi-objective problem.

We address the minimum length scheduling problem in wireless networks, where each transmitter has a finite amount of data to deliver to a common receiver node (e.g., base station). In contrast with previous works that model wireless channels according to the Protocol or Physical model of interference, this paper studies the scheduling problem in multiple access (multi-access) networks. In this kind of network, the receiver node can decode multiple transmissions simultaneously if the transmission rates of concurrent transmitters lie inside the capacity region of the receiver node. We propose a linear programming model that minimizes the schedule length. The model incorporates the capacity region of multiple access channels into scheduling decisions, such that the sum of the transmission rates of simultaneous transmitters is maximized. Because of the high-complexity of the model, we also present a heuristic algorithm, whose performance is extensively evaluated and compared with the optimal solutions.

In sensor networks many efforts have been made on barrier coverage. Most of them rely on the assumption that sensors are randomly or manually deployed around the region of interest. It is obvious that the random deployment wastes many redundant sensors without contribution on the barrier formation. Moreover, in most real scenarios, it is difficult to deploy sensors manually due to the region usually in large scale or in danger. Hence, this paper studies the problem that using mobile sensors to form barrier surrounding the region automatically. The fundamental objective is to take full advantage of the limited number of mobile sensors to form the barrier coverage with the highest detection capability. The challenge is that the sensors only have local information. A fully distributed algorithm based on virtual force and convex analysis is developed for the objective to relocate the sensors from the original positions to uniformly distribute on the convex hull of the region. Simulation results verify the validity of our proposed cooperative scheme.

Cyber-physical systems (CPS) bridge the virtual cyber world with the real physical world. For representing a physical environment in cyber, CPS devices / nodes are assigned to collect data in a region of interest. In practice, the nodes seldom fully cover the region due to the restriction of quantity and cost. Hence, the sampled data are usually inadequate to describe the holistic environment. Recent researches mainly focus on the interpolation methods to generate an approximating model from the raw data. However, in this paper, we propose to study the spatio-temporal distribution of CPS nodes in order to obtain the crucial data for optimal environment abstraction. There are two target problems. First, when the environment changes little over time, what is the optimal spatial distribution of stationary nodes based on historical data? Second, when the environment is time-varying, what is the adaptive spatio-temporal distribution of mobile nodes? We show the NP hardness of the former problem and propose an approximation algorithm. For the latter problem, we develop a cooperative movement algorithm on nodes for achieving a curvature-weighted distribution pattern. A trace driven simulation based on real data of GreenOrbs project evaluates the performance of the proposed approaches.

Delay Tolerant Network (DTN) is a kind of networks that have no fixed path between nodes due to lack of continuous network connectivity. To achieve successful communication and to improve performance in DTN is always a challenge work. Most existing works are based on designing high efficient routing protocols and have not emphasized the MAC layer as well as new technologies available in the Physical layer which may influence the performance for whole networks. In this paper, we consider utilization of Multi-packet Reception (MPR) capability in DTN in order to provide better performance. We introduce our own MPR scheduling scheme, Group Based Scheme (GBS), and compare the performance of DTN with different MPR capabilities. Our works have been evaluated by the simulation using a real vehicular network trace composed of the GPS data of more than 4,000 taxis in Shanghai urban area. The results have demonstrated various impacts of using Multi-Packet Reception (MPR) on performance in DTN. By using MPR in DTN, it outperforms the network without MPR especially when the wireless transmission range increases.

We consider distributed top-k processing problem in a mobile scenario. Specially, we focus on a real application of a bus network (N nodes), where buses are equipped with cameras for real-time security surveillance. Due to the limited number of screens (k, k<;<;N) at the traffic management center, how to select k bus nodes with most passengers to upload image data by DSRC needs to be solved. We present a novel distributed scheme BSS to handle this so-called "top-k node selection" issue in bus network, in which challenges of low time cost and high accuracy is not trivial. BSS utilizes various strategies to speed up top-k node selection facing poor network condition and application requirements. Performance evaluation is carried out on a real-trace driven simulator, which utilizes about 700 buses in Shanghai. The testing results show that BSS has an excellent performance in terms of time cost and average degree of accuracy, which shows the effectiveness of BSS scheme for real-time security surveillance.

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. In this paper, we present a generalized model for the throughput optimization problem in multi-hop wireless networks that support multi-packet reception (MPR) capability. The model incorporates the multi-access channel, which accurately accounts for the achievable capacity of links used by simultaneous packet transmissions. The problem is modeled as a joint routing and scheduling problem. The scheduling subproblem deals with finding the optimal schedulable sets, which are defined as subsets of links that can be scheduled or activated simultaneously. We demonstrate that any solution of the scheduling subproblem can be built with |E| + 1 or fewer schedulable sets, where |E| is the number of links of the network. This result contrasts with a conjecture that states that a solution of the scheduling subproblem, in general, is composed of an exponential number of schedulable sets. Due to the hardness of the problem, we propose a polynomial time scheme based on a combination of linear programming and greedy paradigms. The scheme guarantees the operation of links at maximum aggregate capacity, where the sum of the capacity of the links is maximized and the multi-access channel is fully exploited.

Recent studies have found that using multiple channels can separate concurrent transmissions and significantly improve network throughput. However, these studies have only considered wireless nodes that are equipped with omni-directional antennas, which have high interference. On the other hand, other researchers have found that using directional antennas in wireless networks can reduce interference and improve the network performance. But their studies have only considered single channel to be used in wireless networks with directional antennas. Thus, integrating the two technologies of multiple channels and directional antennas together can potentially bring more benefits. Some previous works have studies the capacity on the multi-channel wireless networks using directional antennas. However, the channel assignment problem of such networks has not been well studied. In this paper, we study the channel assignment problem in the multi-channel wireless networks using directional antennas. In particular, we study the problem: given a set of wireless nodes equipped with directional antennas, how many channels are needed to ensure collision-free transmission? We derive the upper bounds on the number of channels, which depend on the node density. We also construct several scenarios to examine the tightness of the derived bounds. Our result can be used to estimate the number of channels required for a practical wireless network. Besides, our results can also be used to provide a suggestion on the proper node density in the node deployment when the number of channels is given for a wireless network.

In this paper, we present our study of extracting a mobility model for vehicular ad hoc networks (VANETs) from a large amount of real taxi GPS trace data. In order to capture characteristics of the urban vehicle network from microscopic to macroscopic aspects, we design three parameters and extract their values from the GPS trace data. Using this mobility model, we can generate the synthetic trace to simulate the movement of taxis in the urban area of a metropolis. The validation is carried through extensive comparisons between the synthetic trace and the real trace. Validation results show that our mobility model has a good approximation to the real scenario.

Bus lane enforcement system aims to monitor illegal utilization of bus lane by non-permitted vehicles (violator). However, Road-side system leads to considerable infrastructure cost while bus mounted system has limited surveillance coverage. In this paper, we consider an interesting problem in bus mounted system: how to improve the surveillance coverage of bus mounted system without additional infrastructure cost? In other words, we attempt to identify not only the violator immediately in front of the bus, bus also the violators not close to the bus, whose number plates cannot be read directly because of sight blocking of bus mounted cameras. With utilization of communication between bus and existing cameras around intersections, we propose a novel cooperative violator identification scheme, DoubleChecking, with which violators can be sorted out from traffic flow with high accuracy. From theoretical analysis, DoubleChecking shows a good performance for violator identification, which demonstrates the effectiveness of the proposed scheme.

Capacity is one fundamental problem in wireless Ad hoc networks. Deploying directional antennas to wireless networks can reduce interference among concurrent transmissions and increase spatial reuse, while the technology of multi-channel can separate concurrent transmissions. Therefore, combining these two technologies into one wireless network is capable of great improvement on the network capacity. Recent studies proposed a multi-channel network architecture that equips each wireless node with multiple directional antennas, which is called MC-MDA network. The capacity in MC-MDA network is derived under arbitrary and random placements. However, they only used a simplified directional antenna model. For approaching the more accurate capacity in real scenario, in this paper, we consider a hybrid antenna model, which takes the effect of side lobe and back lobe into account. We derive the capacity upper-bounds of MC-MDA networks in arbitrary and random network with the hybrid model to find the effect of back lobe and side lobe. We show that the network capacity is closely related to s the ratio of the radiuses of side lobe to main lobe. The capacity decreases when s increases. Moreover, we compare the network capacity of MC-MDA using the simplified antenna model with our results. When considering the factor of interference constraint, the capacity gain using the hybrid antenna model is √(θ2/θ2+(4π2-θ2)s) over the one with the simplified model.

Initialization is an indispensable process for wireless sensor networks since newly deployed sensors lack a reliable infrastructure for communication. Many efforts have been made to address the problem on initialization. However, most of them fall into the category of energy efficiency. In mission-critical scenarios, it is vital to reduce the initialization time in order to gather information as early as possible. In this paper, we study a new problem of accelerating initialization. First, we provide a novel parallel initialization mechanism that selects cluster heads before deployment and organizes single-hop clusters simultaneously after random deployment. Then, we prior-estimate that the number of cluster head candidates from n sensors is ¿(log n). Finally, simulation results show that these cluster head candidates can achieve the parallel initialization bounded by 16 time slots even for a large n.

Advances in SONET/SDH technologies have introduced novel features for improved services mapping and provisioning, enabling many new avenues for new Carrier Ethernet support. However Ethernet-over-SONET studies have mostly focused on provisioning point-to-point Ethernet private line offerings. This paper considers the more challenging case of provisioning multi-point-to-multi-point Ethernet LAN services over advanced SONET/SDH networks and presents novel strategies based upon connection group overlays. Detailed simulation results are also presented along with directions for future work.

Coverage is a fundamental problem in Wireless Sensor Networks (WSNs). Existing studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a targeted Field of Interest is complex in many real world applications; and yet, existing studies on coverage do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the targeted Field of Interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. In this paper, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, how many sensors are needed to reach a certain expected coverage ratio? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct comprehensive simulations to evaluate the performance of the proposed algorithms.

Multi-packet reception (MPR) offers increased number of concurrent transmissions, which improves the capacity of wireless ad hoc networks. By equipping each node with multiple wireless interface, the transmissions can be separated into different channels and the throughput can be increased. Combining these two technologies, this work studies the capacity of wireless networks wherein each node is equipped with multiple interfaces and each interface can decode at most k concurrent transmissions within its receiving range.

We consider a practical application of intelligent vehicles for intersection traffic control. Specially, we study the intersection traffic control problem using reservation-based intersection traffic control system, which utilizes the information exchange between intelligent vehicles and management agents around the intersections to direct traffic, instead of traffic lights. We focus on how to design an effective passing permission (PP) allocation strategy for this system. In this work, with an observation that will cause this system to be inefficient, we propose a novel look-ahead passing permission allocation strategy (LICP) for intersection traffic control. The large-scale testing results show that LICP can make nearly 25% performance improvement on average intersection delay than the previous first come, first serve method (FCFS).

This article addresses a new area of sensor networks - the anti-sensornet. Though the term anti-sensornet is scattered throughout literature, there is no systematic description of the anti-sensornet. In this article, we provide an introduction to anti-sensornet by analyzing the behaviors of intelligent intruders and the methods used to confront them including hiding sensor networks, enhancing detection capabilities and recovering from successful attacks.

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. In this paper, we present a generalized model for the throughput optimization problem in wireless networks that support multi-packet reception (MPR) capability. Our model directly accounts for nodes with multiple transmitter antennas, which can be directional or omni-directional. We divide the problem into two subproblems: routing and scheduling. Due to the hardness of the scheduling subproblem, we propose a polynomial time heuristic based on a combination of greedy and linear programming paradigms. We use the devised scheme to study the impact of several design parameters on the performance of MPR-capable networks, including the number of interfaces, the beamwidth and the receiver range of the antennas. Numerical results demonstrate the effectiveness and the generality of the scheme, and permit us to draw valuable conclusions about MPR-capable networks.

Currently, vehicles are equipped with forward facing cameras to assist the forensic investigations of events by proactive image capturing from streets and roads. With content redundancy and storage imbalance in this in-network distributed storage system, how to maximize its storage capacity is a challenge. In other words, how to maximize the average lifetime of sensory data (i.e. images generated by cameras) in network is a fundamental problem need to be solved. This paper presents, VStore, a cooperative storage solution for mobile surveillance in vehicular sensor networks (VSN). The mechanisms in VStore are designed for redundancy elimination by exchanging information between vehicles and storage balancing. Compared with previous work, we deal with new challenges in mobile scenario. Field testing was carried out on a real-trace driven simulator, which utilizes about 500 taxies in Shanghai city. The testing results show that VStore can largely prolong the average lifetime of sensory data by cooperative storage.

In this paper, we present a linear programming formulation for the throughput optimization problem in wireless networks that support multi-packet reception (MPR) capability. The formulation takes into account the use of both directional and omni-directional antennas as well as the use of multiple transmitter interfaces per node. The joint routing and scheduling problem is decoupled into routing and scheduling subproblems. We show that the scheduling subproblem is intractable, and propose a polynomial time scheduling algorithm to solve it. We further demonstrate that, for certain type of networks, the completion time of the scheduling algorithm is at most two times the completion time of the the optimal scheduler, which is unknown. We use the proposed scheme for a preliminary study of several design parameters on the performance of MPR-capable networks, including the number of interfaces, the MPR capability and the beamwidth of the antennas.

The capacity of wireless ad hoc networks is mainly restricted by the number of concurrent transmissions. Recent studies found that multi-packet reception (MPR) can increase the number of concurrent transmissions and improve network capacity. This paper studies the capacity of 2-D wireless networks wherein each node can decode at most k simultaneous transmissions within its receiving range. We call such networks k-MPR wireless networks. For comparison, we call traditional networks 1-MPR wireless networks. Suppose that the number of nodes in a wireless network is n and each node can transmit at W bits/sec. For arbitrary k-MPR wireless networks, we show that when k = O(n), the capacity gain over 1-MPR networks is Theta(radickappa). When k = Omega(n), the capacity is Theta(W n) bit-meters/sec and the network is scalable. For random k-MPR wireless networks, we show that when k = O(radic(log n)), the capacity upper bound and lower bound match and the capacity gain over 1-MPR networks is Theta(kappa). When k = Omega(radic(log n)), even the lower bound has a capacity gain of Theta(radic(log n)) over 1-MPR networks. From these results, we conclude that the main constraints for k-MPR wireless networks to utilize MPR ability are the limited number of transmitters and the limited number of flows served by each node.

Abstract The co‐channel interference problem in wireless mesh networks (WMN) is extremely serious due to the heavy aggregated traffic loads and limited available channels. It is preferable for mesh routers to dynamically switch channels according to the accurate estimation of co‐channel interference level in the neighborhood. Most developed interference estimation schemes, however, do not consider the impact of interface switching. Furthermore, the interference in wireless networks has been extensively considered as an all‐or‐nothing event. In this paper, we develop a weighted interference estimation scheme (WIES) for interface‐switching WMN. WIES takes a new version of multi‐interface conflict graph that considers the impacts of frequent interface switching as the interference relationship estimation scheme. Besides, WIES uses a weight to estimate the interference level between links. The weight utilizes two empirical functions to denote the impacts of the relative distance and characteristics of traffic loads in WMN. Extensive NS2 simulations show that WIES achieves significant performance improvements, especially when the interference level of the network is high. We also validate that the interference level of networks is affected by several system parameters such as the number of available channels and the ratio between interference range and transmission range. Copyright © 2008 John Wiley & Sons, Ltd.

The routing problem in wireless mesh networks is concerned with finding "good" source-destination paths. It generally faces multiple objectives to be optimized, such as i) path capacity, which accounts for the bits per second that can be sent along the path connecting the source to the destination node, and ii) end-to-end delay. This paper presents the mesh routing algorithm (MRA), a dynamic programming approach to compute high-capacity paths while simultaneously bounding the end-to-end delay. The proposed algorithm also provides the option of routing through multiple link-disjoint paths, such that the amount of traffic through each path is proportional to its capacity. Simulation results show that MRA outperforms other common techniques in terms of path capacity, while at the same time bounding the end-to-end delay to a desired value.

Currently, vehicular sensor network (VSN) has been paid much attention for monitoring the physical world of urban areas. We have studied VSNs by utilizing about 4000 taxies and 1000 buses equipped with GPS-based mobile sensors in Shanghai to constitute a virtual vehicular sensor network. The communication-connection intermittence makes the routing issue nontrivial when delay-tolerant applications are deployed in VSNs. The existing DTN routing protocols can be categorized as "neighbor-oriented" and how to select a neighbor candidate was always neglected. In this paper, we present a new DTN routing protocol for Delay-Tolerant Vehicular Sensor Networks, Packet-Oriented Routing protocol (POR), which is designed to emphasize neighbor selection based on awareness of packets to be sent and in consideration of probability to complete transferring of these packets. Our results show that POR performs much better than the ordinary Epidemic routing, as well as other popular routing protocols applied in a similar setting.

We study mechanism designs for resource management in service overlay, where services are provided by strategic agents. Usually, resources in distributed systems are limited. However, the current mechanism design does not take the capacity of agents into consideration. Traditionally, the Vickrey- Clarke-Groves (VCG) mechanism has been the only method to design protocols so that each strategic agent will follow the protocols for its own interest to maximize its benefit. We show that the VCG mechanism is not truthful anymore when the capacity of agents is limited. Thus, we have designed, based on non-uniform prices, a new capacity-aware mechanism which subsidizes the service agents so that each agent maximizes its profit if it truthfully reports its cost. Mechanisms for two widely used pricing models are designed and evaluated.

The capacity and flexibility of wireless mesh networks (WMNs) can be greatly improved by adopting dynamic channel assignment. However, dynamic channel assignment brings new challenges such as switching overheads and dependency problems. We propose a channel assignment protocol, called the hybrid channel assignment protocol (HCAP) for infrastructure WMNs (I-WMN). To find out a reasonable tradeoff between flexibility and switching overheads, HCAP adopts static interface assignment strategy for nodes that have the heaviest loads. For other nodes, it adopts a hybrid strategy. HCAP uses a slot-based coordination policy to implement communications between nodes that adopt hybrid strategy. NS2-based simulations show that HCAP not only improves capacity and scalability of I-WMNs, but also enhances per-flow fairness.

The existing vehicular sensors of taxi companies in most of cities can be used for traffic monitoring, however sensors are always set with a long sampling interval because of communication cost saving and network congestion avoidance. In this paper, we focus on the traffic data processing in vehicular sensor networks providing sparse and incomplete information. A performance evaluation study has been carried out in Shanghai by utilizing the sensors installed on 4000 taxis. Two types of traffic status estimation algorithms, the link-based and the vehicle-based, are introduced based on such data basis. The results from large-scale testing cases show that the traffic status can be fairly well estimated based on these imperfect data and we demonstrate the feasibility of such application in most of cities.

The capacity of wireless ad hoc networks is affected by two key factors: the interference among concurrent transmissions and the number of simultaneous transmissions on a single interface. Recent studies found that using multiple channels can separate concurrent transmissions and greatly improve network throughput. However, those studies only consider that wireless nodes are equipped with only omnidirectional antennas, which cause high collisions. On the other hand, some researchers found that directional antennas bring more benefits such as reduced interference and increased spatial reuse compared with omnidirectional antennas. But, they only focused on a single-channel network which only allows finite concurrent transmissions. Thus, combining the two technologies of multiple channels and directional antennas together potentially brings more benefits. In this paper, we propose a multi-channel network architecture (called MC-MDA) that equips each wireless node with multiple directional antennas. We derive the capacity bounds of MC-MDA networks under arbitrary and random placements. We will show that deploying directional antennas to multi-channel networks can greatly improve the network capacity due to increased network connectivity and reduced interference. We have also found that even a multi-channel network with a single directional antenna only at each node can give a significant improvement on the throughput capacity. Besides, using multiple channels mitigates interference caused by directional antennas. MC-MDA networks integrate benefits from multi-channel and directional antennas and thus have significant performance improvement.

Data-intensive Grid applications require huge data transferring between multiple geographically separated computing nodes where computing tasks are executed. For a future WDM network to efficiently support this type of emerging applications, traditional approaches to establishing lightpaths between given source destination pairs are not sufficient because a computing task may be executed on any one of several computing nodes having the necessary resources. Therefore, lightpath establishment has to be considered jointly with task scheduling to achieve best performance. We study the optimization problems of jointly scheduling both computing resources and network resources. We first present the formulation of two optimization problems with the objectives being the minimization of the completion time of a job and minimization of the resource usage/cost to satisfy a job with a deadline respectively. When the objective is to minimize the completion time, we devise an optimal algorithm for a special type of applications. Furthermore, we propose efficient heuristics to deal with general applications with either optimization objective and demonstrate their good performances via simulation.

We present a mobile sensor system for traffic monitoring (GSSTM), which is a typically Grid application in ShanghaiGrid to provide accurate realtime traffic status information. Several issues and challenges in GSSTM are discussed, such as Grid architecture design, data processing for traffic status estimation, storage strategy of massive data, etc. We implemented a prototype system of GSSTM and carried out a field testing in Shanghai city by using GPS-based sensors on 4000 taxis. The testing results show that by utilizing the Grid technology, the expected performance can be obtained, such as stability, scalability, etc. Meantime, traffic status can be fairly well estimated in GSSTM.

The application of directional antennas in wireless ad hoc networks brings numerous benefits, such as increased spatial reuse and mitigated interferences. Most MAC protocols with directional antennas are based on the RTS/CTS mechanism which works well in wireless ad hoc networks using omnidirectional antennas. However, RTS/CTS frames cannot mitigate the interference completely. Besides, they also contribute a lot to the performance overhead. This paper studies the problem from a new perspective. We have found that the transmission success probability under directional transmission and directional reception is quite high when the antenna beamwidth is quite narrow. Motivated by the analytical results, we design a lightweight MAC protocol without RTS/CTS frames. The preliminary results demonstrate that this new protocol performs better than MAC protocols based on the RTS/CTS mechanism. The results also show that a collision-tolerant transmission is feasible under the narrow beam configuration.

In performance studies of vehicular ad hoc networks (VANETs), the underlying mobility model plays an important role. Since conventional mobile ad hoc network (MANET) routing protocols do not work efficiently in vehicular environments due to the rapid topology changes, the Delay-Tolerant Network (DTN) model is often applied. In this paper, we construct a new mobility model, the Shanghai Urban Vehicular Network (SUVnet) model by using the GPS data from more than 4,000 taxis we have collected, and then investigate the performance of two kinds of DTN routing, the non-geographic pure epidemic routing and our newly-proposed geographic DTN routing, the Distance-Aware Epidemic Routing (DAER). We use the popular random waypoint mobility model and a more complex microscopic traffic simulator generated model for performance comparison. With the two considered DTN routing protocols, conventional mobility models tend to give higher performance results than SUVnet model, the presumably more realistic mobility model.

A disruption-tolerant network (DTN) attempts to route packets between nodes that are temporarily connected. Difficulty in such networks is that nodes have no information about the network status and contact opportunities. The situation is different in public bus networks because the movement of buses exhibits some regularity so that routing in a deterministic way is possible. Many algorithms use a contacts oracle that provides the exact meeting times and durations between all nodes. However, in a real vehicular environment, an oracle is not always accurate, and deterministic routing gives poor results. In this paper, we present BLER, a routing algorithm that achieves effective routing in a buses environment. BLER, compared to other algorithms, performs routing at bus line level instead of bus level; it uses specific bus lines information to achieve good performances. We evaluate BLER on real traces of the bus network of Shanghai, and compare it to other routing algorithms. Performances provide good results for this kind of DTNs.

Applying directional antennas in wireless ad hoc networks offers numerous benefits, such as extended communication range, increased spatial reuse, improved capacity and suppressed interference. However, directional antennas can cause new location-dependent carrier sensing problems, such as new hidden terminal and deafness problems, which can cause severe penalties to the performance. Recently, a few schemes have been proposed to tackle these problems. However, these methods can provide limited solutions on the hidden terminal and deafness problems. We propose a new MAC protocol, termed the busy-tone based directional medium access control (BT-DMAC) protocol. When the transmission is in progress, the sender and the receiver will turn on their omnidirectional busy tones to protect the transmission. By combining with directional network allocation vector (DNAV), the scheme almost mitigates the hidden and the deafness problems completely. The mechanism increases the probability of successful data transmission and consequently improves the network throughput. This paper describes the BT- DMAC scheme and analyzes its performance. The simulation results also demonstrate the effectiveness of the protocol.

This paper presents the FFT (flip-flop tone) DMAC protocol, a tone based MAC protocol using directional antennas to solve the deafness problem, hidden terminal and exposed terminal problems simultaneously. It uses two pairs of flip-flop tones. The first pair of tone is sent omni-directionally to reach every neighboring node to announce the start and the end of communication, and therefore to avoid the deafness problem. The second pair of tone is sent directionally towards the sender. It is used to solve the hidden terminal problem as well as the exposed terminal problem. Evaluation shows that FFT-DMAC can achieve better performance compared to the 802.11 and ToneDMAC protocol.

The number of available channels in a specific wireless network is bounded. Therefore, co-channel interference is inevitable in most wireless networks. The co-channel interference problem in wireless mesh networks is more serious than that in single-hop wireless networks. It is preferable for mesh nodes to dynamically adjust channels to elevate the interference level of networks. In this paper, we introduce the conception of communication constraint to denote the strategies that decide the mode of interface switching. We develop an interference estimation scheme. The proposed scheme uses a weight to represent the effects of loads distribution and relative distance between nodes on the interference level of network. We compare the performance of our solution with the model presented by Gupta and Kumar [14] through both graph-based and NS2 simulations. Extensive results show that our solution achieves better performance than the compared scheme when the number of available channels is small.

Exposed and hidden terminal problems decrease the performance of 802.11 MAC protocol in mobile ad hoc network (MANET). We figure out that the two problems are due to a single fundamental reason, that is, 802.11 uses an oversimplified interference model. We propose an interference-aware busy tone based MAC protocol (IABT) to improve the throughput of MANET. Encoding the interference information in the frequency, a busy tone will reserve the space necessary for the ongoing transmission. Our protocol can solve both the hidden and exposed terminal problem simultaneously. Examined by NS2 simulator, the protocol can reduce the MAC collisions and improve the performance substantially

In this paper, we address the following problem: given a set of wireless nodes on the plane, what is the number of channels needed to ensure a schedulable conflict-free communication? While the exact number of channels needed is very difficult to compute, we focus on an upper bound for this number, in terms of the node density D. In particular, we derive several upper bounds in terms of D based on different settings of network interference ratio r, and analyze the trend of the upper bound as r changes. We also examine the tightness of our upper bound, by studying some specific scenarios of transmission. Our result can be viewed as another theoretical investigation towards the relationship between the network performance and the number of available channels. In practical application, the result suggests an easy estimation on the number of channels needed in the configuration of a wireless network. On the other hand, when the number of channels in a wireless system is given, the result can be used to provide a suggestion on the node density in the node deployment.

It is crucial to design algorithms for energy-efficient query processing for a sensor network since sensor nodes are battery-powered, and thus their lifetimes are limited. We propose a history-based approach to optimizing query processing. We apply the approach to the top-k query problem and design new algorithms. Energy consumption can be reduced by pruning unnecessary sub-queries or guiding the query to right directions. Simulation results show that energy cost can be significantly reduced. This approach can be generalized for other query problems.

It is widely believed that IP over optical networks will be a major component of the next generation Internet However, it is not efficient to map a single multicast IP flow into one light-tree, since the bandwidth of an IP flow required is usually much less than that of a light-tree. In this paper, we study the problem of multicast flow aggregation (MFA) in the IP over optical two-layered networks under the overlay model, which can be defined as follows: given a set of head ends (i.e. optical multicasting sources), each of which can provide a set of contents (i.e. multicast IP flows) with different required transmission bandwidth, and a set of requested content at the access routers (i.e. optical multicasting destinations), find a set of light-trees as well as the optimal aggregation of multicast IP flows in each light-tree. We model MFA by a tri-partite graph with multiple criteria and show that the problem is NP-complete. Optimal solutions are designed by exploiting MFA to formulate an integer linear programming (ILP), with two parameters: the multicast receiving index alpha and the redundant transmitting index beta. We also propose a heuristic algorithm. Finally, we compare the performance of MFA for different combination of alpha and beta via experiments and show our heuristic algorithm is effective for large-scale network in numerical results

In this work, we study the topology enhancement problem of wireless sensor networks. Our research focuses on reducing the path-based vulnerability. The objective is to get as much information as possible for any target moving out of the surveillance area. We study intelligent targets that have the knowledge of existing sensor distribution. Under such assumption, we find the vulnerable paths that rational targets would like to take and block these paths to enhance the network detection performance. Simulation shows that by adding only a few numbers of new sensors on specific positions, we can greatly decrease the vulnerability of a randomly deployed sensor network.

Abstract The goal of the ShanghaiGrid is to provide information services to the people. It aims to construct a metropolitan‐area information service infrastructure and establish an open standard for widespread upper‐layer applications from both communities and the government. The Information Service Grid Toolkit and a typical application called the Traffic Information Grid are discussed in detail. Copyright © 2005 John Wiley & Sons, Ltd.

Continuous media, such as digital movies, video clips, and music, are becoming an increasingly common way to convey information, entertain and educate people. However, limited system and network resources have delayed the widespread usage of continuous media. Most existing on-demand services are not scalable for a large content repository. In this paper, we propose a scalable and inexpensive video delivery scheme, named Scheduled Video Delivery (SVD). In the SVD scheme, users submit requests with a specified start time. Incentives are provided so that users will specify the start times that reflect their real needs. The SVD system combines requests to form the multicasting groups and schedules these groups to meet the deadline. SVD scheduling has a different objective from many existing scheduling schemes. Its focus has been shifted from minimizing the waiting time toward meeting deadlines and at the same time combining requests to form multicasting groups. SVD compliments most existing video delivery schemes as it can be combined with them. It requires much less resources than other schemes. Simulation study for the SVD performance and the comparison to other schemes are presented.

Sensor networks are expected to find widespread use in a variety of applications. In the near future, more and more sensor networks will be deployed for multiple services in a surrounding area. Different sensor networks may overlap or partially overlap with each other, and may interfere with each other. However, it also provides an opportunity to construct a low energy, high connectivity, and more robust sensor network. We propose an InterSensorNet scheme, which is a federation of multiple sensor networks. The success of the InterSensorNet depends on whether a node is willing to cooperate with nodes in a foreign network. What will be the incentive to do so? With concepts from economics and game theory, we propose a mechanism design (MD) approach to handle the strategic agents that respond to incentives. We apply MD to two practical setting of multiple sensor networks and study applications of InterSensorNet mechanism design.

To turn an ideal mobile sensor network simulation model into reality, terrain is one of the inevitable factors we have to consider. This work is the first attempt to address this issue, by studying the behavior of mobile sensors under constraints of terrain. Specifically, we study the power consumption and the loss ratio of mobile sensors when they are moving on different terrains. These two metrics are utilized to select the best destinations, the best paths to the destinations, and match these destinations with mobile sensors.

RPP is a distributed routing mechanism for wireless ad hoc networks with strategic agents. A strategic agent is rational but selfish and has its own incentive to route traffic for other agents. Forwarding data is definitely not the self-interest of an agent due to consumption of battery. However, if no agent is willing to forward data for others, the ad hoc network itself will break. In this paper, a routing mechanism is designed such that maximizing the benefit of each strategic agent leads to a global optimal system. In this mechanism, an agent accepts payments for forwarding data for other agents if the payment covers their costs. The payment is computed recursively to obtain a cost-efficient and truthful mechanism. The overpayment in current mechanisms is completely removed. Analysis and simulation results are also provided.

Overlay multicast has drawn a lot of attention as an alternative to IP multicast. In the recently proposed overlay multicast solution, most of the implementations do not address the inter-domain administrative issue. For wide area multicast, it is unavoidable to have communication among several administrative domains. At this time, multicast routing cannot assume the knowledge of the global network topology, nor can it ignore the routing policy constrained by contractual commercial agreement between administrative domains. This problem can be solved by an hierarchical organization of the routing infrastructure. We propose a zone approach to overlay multicast that performs hierarchical routing on two levels - one level for within each of the administrative domains and another level for among the domains. We compare a single centralized routing to our zone-approach routing for overlay multicast and demonstrate that the performance penalty for the hierarchical organization of routing is small. The results have shown that our distributed design can achieve very close performance to the centralized algorithm. At the same time it enables the composition of different administrative networks, each with its own independent multicast protocol and administrative policy.

Multicast is important for realtime delivery of popular streaming media. Proxy-based multicast is an approach to implementing the multicast function by proxy servers at application levels. Many routing algorithms have been proposed to build an overlay network when proxy locations have been determined. On the other hand, the problem of where to place proxies has not been extensively studied yet. A study on the placement problem for proxy-based multicast is presented in this paper. Performance metrics are studied. A number of algorithms are proposed for multicast proxy placement and their performance is compared.

Scalability is a key issue of on-line video service. Different methods have been proposed for the closed-loop video service, such as batching and patching. However, scalability itself has not been formally defined and the condition for a scalable system is not well studied yet. This paper studies scalability and the condition for scalable systems. It is found that when the arrival rate is across the threshold /spl Theta/ the system becomes scalable. This threshold value depends only on the number of videos, video length, and request distribution. We present an analysis for batching and patching. Comparison of batching, patching, and a new method, scheduled video delivery (SVD), is presented in this paper.

A video distribution architecture with edge stations and Wi-Fi delivery networks as connection to residential homes is described in this paper. An edge station is configurable and controllable by a central server. It is placed in the access point of Wi-Fi networks and has multiple usages. Due to its large storage space it can be used as a mirroring/caching proxy for continuous media. Replicating popular video contents in many small mirroring sites can significantly reduce the network bandwidth requirement. When this structure is used with a cable or hybrid fiber/coax (HFC) network, it turns to be a hybrid fiber/coaxAVi-Fi (HFCW) structure, particularly suitable for video-on-demand (VoD) applications. There are many advantages of this architecture. First, the central server load and network traffic can be reduced to a small percentage. Second, the response time of most video playing requests can be significantly reduced. Third, true VoD can possibly be provided with a one-way cable system. Finally, noise from STBs (set-top boxes) is absorbed by the edge station in a two-way cable system.

In contrast to one-to-one unicast transmission, multicast provides a one-to-many transmission paradigm. The overlay multicast has drawn a lot of attention as an alternative to IP multicast. In this paper, we compare four typical implementations of overlay multicast: Scattercast, Narada, overcast and ALMI. Several metrics are compared which are applicable to these projects, including relative delay penalty, normalized resource usage and stress. We evaluate those projects in a uniform network environment and discuss how the design approach and structure affects the performance.

Abstract Video servers are essential in video‐on‐demand and other multimedia applications. In this paper, we present our high‐performance clustered CBR video server, Odyssey. Odyssey is a server connecting PCs with switched Ethernet. It provides efficient support for normal play and interactive browsing functions such as fast‐forward and fast‐backward. We designed a set of algorithms for scheduling, synchronization and admission control, which results in a high utilization of resources. Odyssey is able to deliver a large number of video streams. Copyright © 2003 John Wiley & Sons, Ltd.

Most existing on-demand video services, such as VoD, batching and patching, are not scalable. Although the near VoD service is scalable, it can provide only dozens of videos. A scalable video delivery paradigm, named scheduled video delivery (SVD), is described. In the SVD paradigm, users submit requests with specification of start time. The SVD system combines requests to form multicasting groups and schedules these groups to meet the deadline. SVD is not only scaled to the number of users but also to the number of video objects. Furthermore, SVD can serve more clients with mirroring proxies.

Continuous media, such as digital movies, video clips, and music, are becoming an increasingly common way to present information, entertain and educate people. However, limited system and network resources have delayed the widespread usage of continuous media. In this paper, we propose a scalable and inexpensive video delivery paradigm, named Scheduled Video Delivery (SVD). In the SVD paradigm, users submit requests with specification of start time. The provider schedules these requests to meet the QoS specification and to maximize utilization of the resources. SVD scheduling has a different objective from many existing scheduling schemes. It does not aim at minimizing the waiting time. Instead, it focuses on meeting deadlines and at the same time combining requests to form multicasting groups. SVD scales not only to the number of users but also to the number of video objects.

Providing efficient support for interactive browsing operations such as fast-forward (ff) and fast-backward (fb) is essential in video-on-demand and other multimedia server systems. In this paper, we propose two basic approaches to scheduling interactive browsing operations: 1) the prefetching approach and 2) the grouping approach. Block-skipping and frame-skipping algorithms are presented for constant bit rate (CBR) data blocks. These algorithms can precisely schedule video streams for both normal play and interactive browsing operations.

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.

A mapping algorithm for heterogeneous computing systems is proposed in this paper. This algorithm utilizes a new indicator-the relative cost-to obtain optimal mapping. The existing Min-min algorithm can be well explained under synergy of this new indicator. It is found that the Min-min algorithm leaves room for improvement because of its haste to reduce completion time by overlooking the impact of load balance. Our new algorithm retains the advantages of the Min-min algorithm and balances the load very well. It demonstrates the ability to generate good mapping in various heterogeneous environments.

Scheduling DAGs to multiprocessors is one of the key issues in high-performance computing. Most realistic scheduling algorithms are heuristic and heuristic algorithms often have room for improvement. The quality of a scheduling algorithm can be effectively improved by a local search. In this paper, we present a fast local search algorithm based on topological ordering. This is a compaction algorithm that can effectively reduce the schedule length produced by any DAG Scheduling algorithm. Thus, it can improve the quality of existing DAG scheduling algorithms. This algorithm can quickly determine the optimal search direction. Thus, it is of low complexity and extremely fast.

A runtime parallel incremental DAG scheduling approach is described in this paper. A DAG is expanded incrementally, scheduled, and executed on a parallel machine. A DAG scheduling algorithm is parallelized to scale to large systems. In this approach, a large DAG can be executed without consuming large amount of memory space. Inaccurate estimation of task execution time and communication time can be tolerated. This runtime approach can also execute dynamic DAGs. Implementation of this parallel incremental system demonstrates the feasibility of this approach. Preliminary results show that it is superior to other approaches.

In this paper, we present the performance result and analysis of parallel CBR video servers with conflict-free scheduling algorithms and server synchronization at time slot and time cycle levels.The experimental outcome shows that by relaxing serverlevel synchronization to a time cycle, the conflict-free slot scheduling produces higher stream throughput than the cycle scheduling.

This paper presents scheduling algorithms for normal and interactive playout in parallel video servers. These algorithms include conflict-free scheduling, delay minimization, and request relocation for normal playout, as well as prefetching and grouping for interactive operations. Performance is presented for these algorithms.

The lack of a versatile software tool for parallel program development has been one of the major obstacles for exploiting the potential of high-performance architectures. In this paper, we describe an experimental software tool called CASCH (Computer Aided SCHeduling) for parallelizing and scheduling applications to parallel processors. CASCH transforms a sequential program to a parallel program with automatic scheduling, mapping, communication, and synchronization. The major strength of CASCH is its extensive library of scheduling and mapping algorithms representing a broad range of state-of-the-art work reported in the recent literature. These algorithms are applied for allocating a parallelized program to the processors, and thus the algorithms can be interactively analyzed, tested and compared using real data on a common platform with various performance objectives. CASCH is useful for both novice and expert programmers of parallel machines, and can serve as a teaching and learning aid for understanding scheduling and mapping algorithms.

Scheduling DAGs to multiprocessors is one of the key issues in high-performance computing. Local search can be used to effectively improve the quality of a scheduling algorithm. In this paper, based on topological ordering, we present a fast local search algorithm which can improve the quality of DAG scheduling algorithms. This low complexity algorithm can effectively reduce the length of a given schedule.

Parallel scheduling is a new approach for load balancing. In parallel scheduling, all processors cooperate to schedule work. Parallel scheduling is able to accurately balance the load by using global load information at compile-time or runtime. It provides high-quality load balancing. This paper presents an overview of the parallel scheduling technique. Scheduling algorithms for tree, hypercube, and mesh networks are presented. These algorithms can fully balance the load and maximize locality at runtime. Communication costs are significantly reduced compared to other existing algorithms.

Most static algorithms that schedule parallel programs represented by macro dataflow graphs are sequential. This paper discusses the essential issues pertaining to parallelization of static scheduling and presents two efficient parallel scheduling algorithms. The proposed algorithms have been implemented on an Intel Paragon machine and their performances have been evaluated. These algorithms produce high-quality scheduling and are much faster than existing sequential and parallel algorithms.

Runtime Incremental Parallel Scheduling (RIPS) is an alternative strategy to the commonly used dynamic scheduling. In this scheduling strategy, the system scheduling activity alternates with the underlying computation work. RIPS utilizes the advanced parallel scheduling technique to produce a low overhead, high quality load balancing, as well as adapting to irregular applications. The paper presents methods for scheduling a single job on a dedicated parallel machine.

Runtime incremental parallel scheduling (RIPS) is a new approach for load balancing. In parallel scheduling, all processors cooperate together to balance the workload. Parallel scheduling accurately balances the load by using global load information. In incremental scheduling, the system scheduling activity alternates with the underlying computation work. RIPS produces high-quality load balancing and adapts to applications of nonuniform structures. This paper presents methods for scheduling a single job on a dedicated parallel machine.

Abstract A run‐time support is necessary for parallel computations with irregular and dynamic structures. One important component in the support system is the run‐time scheduler which balances the working load in the system. We present a new algorithm, Symmetrical Hopping, for dynamic scheduling of ultra‐lightweight processes. It is a dynamic, distributed, adaptive and scalable scheduling algorithm. This algorithm is described and compared to four other algorithms that have been proposed in this context, namely the randomized allocation, the sender‐initiated scheduling, the receiver‐initiated scheduling, and the gradient model. The performance of these algorithms on Intel Touchstone Delta is presented. The experimental results show that the Symmetrical Hopping algorithm achieves much better performance due to its adaptiveness.

One of the essential problems in parallel computing is: Can SIMD machines handle asynchronous problems? This is a difficult, unsolved problem because of the mismatch between asynchronous problems and SIMD architectures. We propose a solution to let SIMD machines handle general asynchronous problems. Our approach is to implement a runtime support system which can run MIMD-like software on SIMD hardware. The runtime support system, named P kernel, is thread-based. There are two major advantages of the thread-based model. First, for application problems with irregular and/or unpredictable features, automatic scheduling can move some threads from overloaded processors to underloaded processors. Second, and more importantly, the granularity of threads can be controlled to reduce system overhead. The P kernel is also able to handle bookkeeping and message management, as well as to make these low-level tasks transparent to users. Substantial performance has been obtained on Maspar MP-1.>

This paper describes the design of the Fortran90D/HPF compiler, a source-to-source parallel compiler for distributed memory systems being developed at Syracuse University. Fortran 90D/HPF is a data parallel language with special directives to specify data alignment and distributions. A systematic methodology to process distribution directives of Fortran 90D/HPF is presented. Furthermore, techniques for data and computation partitioning, communication detection and generation, and the run-time support for the compiler are discussed. Finally, initial performance results for the compiler are presented. We believe that the methodology to process data distribution, computation partitioning, communication system design, and the overall compiler design can be used by the implementors of compilers for HPF.

One of the essential problems in parallel computing is: can STMD machines handle asynchronous problems? Thas is a dificult, unsolved problem because of the mismatch between asynchronous problems and SIMD archtiectures. We propose a soluiaon to let SIMD machines handle general asynchronous problems. Our approach is to implement a runtime support system which can run MIMD-like software on SIMD hardware. Substantial performance has been obtained on CM-2 and CM-5.

Programming assistance, automation concepts, and their application to a message-passing system program development tool called Hypertool are discussed. Hypertool performs scheduling and handles the communication primitive insertion automatically, thereby increasing productivity and eliminating synchronization errors. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers improve their algorithms and programs.>

As the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error prone. Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. Parallel models of computation, parallelization problems, and tools for computer-aided programming (CAP) are discussed. As an example, a CAP tool that performs scheduling and inserts communication primitives automatically is described. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs.>

A novel, systematic approach to sequential MOS circuit design is described. The approach aims at minimizing the number of transistors needed to implement a given switching function. The techniques used are based on the switching network logic (SNL) approach, which was previously introduced as a systematic method of designing MOS combinational logic circuits. The authors extend the approach to sequential MOS circuit design. Examples are given to show that the proposed method reduces the number of MOSFETs as compared to existing design methods. A CAD (computer-aided design) program based on the SNL approach is presented as a finite state machine generator.>

A net-merging method is proposed for gate-matrix layout. The method is based on a density function to calculate the minimum number of tracks necessary for the net assignment. Examples that demonstrate how this method leads to a denser gate matrix layout are included. The pitch of the gate columns is determined by the design rules, in particular, the space required to accommodate a diffused region with an internal contact window between two poly columns. In gate matrix layout, when the space between a pair of gate columns is not sufficient, the spacing between them can be increased locally to two pitches to avoid design rule violation as long as such a local change does not cause column matching problems.>

Abstract The theory of directed switching networks is developed. After the fundamental concepts are defined, the rank of the path matrix of a Directed Single‐Contact (Di‐SC) network is discussed. The theorem which gives the essential relationship between the SC and Di‐SC networks is proved. Then the Odd‐Addition‐Subtraction (OAS) condition is given as a necessary condition for Di‐SC functions. The analysis of directed switching networks and the synthesis of Di‐SC networks are also given for practical applications. The results could be used in the design of electrical and computer systems with directed elements.