Knowledge on water-use patterns in residential settings can help policymakers formulate well-targeted water conservation measures and evaluate the efficacy of such measures. Prior studies have applied machine learning techniques to disaggregate household-level water consumption data, collected by smart meters, into specific end-use categories, such as showering, basin use, kitchen use, and use by washing machine. However, the analysis of the effect of the sampling interval on the classification accuracy level remains an underinvestigated issue. This article seeks to fill this knowledge gap by identifying an optimal sampling interval that can achieve a high level of classification accuracy while overcoming constraints of on-device data storage, data transmission, and energy consumption. To understand the benefits of collecting fine-grained data for machine learning, we have built a high-resolution tap-based Internet of Things (IoT) system comprising a set of Wi-Fi-based tap sensors, gateway infrastructure, and a secure data processing pipeline. Based on empirical tap-based data collected over an eight-month period, we concluded that when the sampling interval decreases slightly from 5 to 1 s, the accuracy level of the end-use classification model increases significantly from 66.6% to 76.1 %. This article also highlights the challenges of deploying IoT sensors to collect water consumption data in a domestic setting. In order to collect sufficient ground-truth data for the training and verification of a generalizable water end-use disaggregation model, it is necessary to sophisticate the flow data collection system by adopting low-power wide-area network technologies and reducing the level of energy consumption of the flow sensing components.

Wireless sensor networks have enabled smart infrastructures and novel applications. With the recent roll-out of Narrowband IoT (NB-IoT) cellular radio technology, wireless sensors can be widely deployed for data collection in cities around the world. However, empirical evidence regarding the coverage and connectivity of NB-IoT in dense urban areas is limited. This article presents an empirical study that focuses on evaluating the coverage and connectivity of NB-IoT in a dense urban environment. We have designed an NB-IoT sensor node and deployed over 100 of them in high-rise apartment buildings in Hong Kong. These sensor nodes utilize a commercial NB-IoT network to collect high-resolution water flow data for machine learning model training and provide timely feedback to users. We collect and analyze the empirical NB-IoT signal measurements from the sensor nodes deployed in various challenging outdoor and indoor environments for over three months. These empirical measurements reveal correlations between NB-IoT connectivity and sensor installation environments. We also observe that inter-cell interference, as a result of coverage by multiple neighboring NB-IoT cells in a dense urban environment, is a source of connectivity degradation. We discuss potential issues that IoT application designers and system integrators might encounter in practical NB-IoT devices deployment, and we propose a transmission decision algorithm based on signal measurements for mitigating energy wasted due to transmission failures. Finally, we demonstrate the results and the benefits of using high-resolution water flow data collected by our purpose-built NB-IoT sensor nodes for studying the patterns of domestic water consumption in Hong Kong.

The rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population Optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.

As hearing loss is arousing more and more public concern, different researches have been conducted on translating the sign language into spoken language. However, most of these researches remain in a theoretical level and few of them investigate how to realize a real system. In this paper, we introduce an effective and portable Hong Kong sign language recognition platform which can translate the Hong Kong sign language within a few seconds. In this platform, there are mainly two parts: a mobile application and a Jetson Nano. The mobile application accounts for preprocessing the sign video and transferring the videos to Jetson Nano. Then, Jetson Nano will translate sign videos into spoken language with the pretrained deep learning model and return the results to the mobile application. With this platform, non-disabled people can easily translate and understand the sign performed by deaf people through mobile phones quickly. We believe that this platform can significantly facilitate the daily communication between deaf people and the others in Hong Kong.

With the rapid development in wearable technology, wearable devices integrating with various sensors have been broadly applied in different areas. Yet there is seldom any previous study which focuses on applying wearable devices and deep learning in learning analytics. This paper considers a sophisticated real-time learning analytics platform for analyzing students' learning states and learning activities with wearable devices and deep learning. During the experimental period of this platform, students will receive instant notifications from an intelligent mobile application when their heart rate are out of their normal range so that the actual learning activities conducted by students can be collected to train deep learning models for recognizing their learning activities. At the same time, students can enjoy the sleeping monitoring and the exercise monitoring functionalities provided by the smart watches in this platform. The results of the interviews conducted after the experiment for this platform demonstrate that 89% of students think that this platform is useful for their daily lives and 65% of students report that this platform brings positive effects on their learning in different aspects. More importantly, this work sheds lights on the possibility of applying wearable devices in learning analytics to improve the learning effectivenesses and life qualities of students.

The platoon-based driving pattern, a cooperative driving pattern for a leader and a number of followers, is a good way to deal with some traditional traffic issues and brings many benefits when performing special platoon tasks. However, it's possible for each platoon member to be fully controlled by attackers, especially the leader, which may cause severe damage to the platoon stability. In this paper, we propose a vehicle platoon leader updating scheme in vehicular networks based on blockchain techniques and reputation management mechanism, so as to ensure that the most trusted platoon member acts as leader. In this scheme, according to received warning messages of traffic events, each platoon member evaluates others' reputations in the form of offsets. Using the designed reputation blockchain with Delegated Proof-of-Stake (DPoS) consensus scheme, miners generate blocks in turn and wait for others' verification. If a block successfully passes the consensus process, it will be formally added to the blockchain, which can reflect all platoon members' current reputation values. In addition, we also have to guarantee that the miner group is composed of several platoon members with high reputation and the leader serves as a miner. In this way, the scheme both meets the real-time requirement of reputation management and saves communication overhead compared with existing schemes. Finally, we analyze the proper functioning and security of our proposed scheme.

The technology advancement has made Unmanned Aerial Vehicle (UAV) swarm a promising method to achieve complicated missions that a single UAV cannot support. Leader-followers formation is a widely used swarm management scenario where a leader drone frequently broadcasts controlling messages to all follower drones to achieve collaboratively a common mission. However, managing such a UAV swarm, especially when the member drones dynamically join and leave the swarm, introduces significant security challenges and performance overhead.In this work, we propose a Swarm Broadcast Protocol (SBP) to facilitate the security protection of leader-followers formation based UAV swarms. SBP contains a security key management scheme that manages a broadcast key among the swarm for leader to broadcast encrypted messages to followers. When swarm membership changes, the broadcast key will be updated and synchronized among the swarm to maintain both backward and forward secrecy. The overhead of SBP is small that only constant computational overhead is needed for both swarm leader and followers to achieve key synchronization when a new drone joins regardless of the current swarm size. This feature would highly reduce the overhead when there are many individual drone joining events. Through experiments on network emulator, we show that SBP achieves lowest bandwidth overhead and CPU utilization to handle multiple swarm membership changing events, comparing with two public-key-based swarm management protocol baselines.

As reported by Hong Kong Government in 2017, there are more than 1.5 million residents suffering from hearing impairment in Hong Kong. Most of them rely on Hong Kong Sign Language for daily communication while there are only 63 registered sign language interpreters in Hong Kong. To address this specific social issue and also facilitate the effective communication between the hearing impaired and other people, this paper introduces a word-level Hong Kong Sign Language(HKSL) dataset which currently includes 45 isolated words and at least 30 sign videos per word performed by different signers(more than 1500 videos in total now and still enlarging). Based on this dataset, this paper systemically compares the performances of various deep learning approaches, including (1) 2D histogram of oriented gradients(HOG) feature/pose estimation/feature extraction with long-short term memory(LSTM) layer; (2) 3D Residual Neural Network(ResNet) (3) (2+1)D Residual Neural Network, in HKSL recognition. Meanwhile, to further improve the accuracy of sign language recognition, this paper proposes a novel method called (3+2+1)D ResNet Model with Frame Selection which adopts blurriness detection with Laplacian kernel to construct high-quality video clips and also combines both (2+1)D and 3D ResNet for recognizing the sign language. At the end, the experimental results show that the proposed method outperforms other deep learning approaches and attains an impressive accuracy of 94.6% in our dataset.

The physical layer security of OFDM communication systems is getting more and more attention. Protecting the channel from eavesdroppers has received some attention. Artificial noise is generally considered to utilize the null space of the intended receiver's channel to interfere with the eavesdropper's channel, but it may not be able to defend against passive eavesdroppers. In this paper, a novel anti-eavesdropping OFDM system is proposed by using artificial pilot noise. Pilots play an important role in demodulating OFDM signal. In our proposed scheme, an auxiliary device called Helper would generate noise signals that overlap on the pilots sent by the user terminal. With the knowledge of the signals sent by the Helper, the receiver can decode the user terminal signal but eavesdroppers cannot. This would prevent eavesdroppers from overhearing the information transmitted between the user terminal and the receiver, so as to provide a secure communication for the user terminal. Finally, we use wireless open access research platform (WARP) v3 to test the feasibility and security of the scheme in practice. Our experimental results show that the proposed scheme can provide a secure communication in the environments with passive eavesdroppers.

With the popularity of wearable devices, smart watches containing various sensors have been widely adopted for many healthcare applications. Yet there is rarely any research study on the possible uses of smart watches for learning analytics, particularly for analyzing students' learning activities through the physiological and/or movement data collected on their smart watches. This paper considers a pioneering and sophisticated learning analytics platform using fine-tuned deep learning models to predict students' learning activities based on the real-time data, including their heart rates, calories, three-axis accelerometer and gyroscope data, captured on wearable devices and then uploaded onto a cloud server for thorough analyses. To validate on the actual activities conducted by each student, an intelligent mobile application is developed to push instant notifications for students to report their own activities whenever the change of heart rates are deviated significantly from their normal values. Based on students' heart rates and calories, a long-short term memory (LSTM) model is built to classify students' learning states as active or not with an impressive prediction accuracy of 95% whereas another hybrid model combining both the LSTM and convolutional neural networks attains the highest prediction accuracy of 74% to predict students' specific learning activities as based on their physiological and movement data. The prototype implementation clearly demonstrates the feasibility of the proposed framework for learning analytics. More importantly, this work shed lights on various directions including the integration of noise filters to preprocess the collected data for further investigation.

Cellular Vehicle-to-Everything (C-V2X) as a wireless communication technology has been widely investigated to guarantee road safety and support autonomous driving. Nevertheless, due to high-mobility of nodes and time-varying topology of networks, C-V2X networks always face many security threats. We identify a new Denial of Service (DoS) attack in which a small number of nodes maliciously reserve the communication resources in a C-V2X network by requesting high priority V2X services frequently. A malicious vehicle detection scheme is then developed to defend against this attack. Different from the detection methods based on traffic analysis, the Mobile Edge Computing (MEC) server only needs to focus on detecting nodes with high malicious probability in our scheme. Thus, we propose a model to estimate the probability that a node is malicious based on its communication resources consumption state. Simulation results demonstrate not only the feasibility of our attack but also desirable performance of the proposed malicious node detection mechanism.

In this paper, we study the distributed caching scheme in dense small cell networks, that determines which files each SBS should cache locally. In particular, we consider that the appropriate caching policy would produce more Coordinated Multiple Points Joint Transmission (CoMP-JT) opportunities so as to reduce wireless access resource consumption. Our caching scheme minimizes the wireless resource consumption while satisfying the caching capacity limit. Then we formulate the optimal caching problem into a submodular function subject to matroid constraints, which can be solved by an efficient algorithm. Finally we conduct extensive simulations to demonstrate that our proposed distributed caching scheme with considering the impact of CoMP-JT is effective for reducing wireless resource consumption.

Small cell network is a promising approach to improve network capacity due to the high link quality and spectrum utility efficiency. Nevertheless, the bandwidth enjoyed by small cell networks is limited by the wireless backhaul capacity and the serious interference from neighbor cells. The backhaul resource needed can be reduced by caching in small cell base stations (SBSs). On the other hand, the inter-cell interference can be mitigated by Coordinated Multi-Point-Joint Transmission (CoMP-JT) which allows multiple SBSs transmit concurrently. Unfortunately, CoMP-JT requires more backhaul resources and caching capacity. This paper jointly considers the transmission mode selection and the caching scheme to minimize wireless backhaul resources consumption. Different from existing works, our work identifies the optimal caching placement while satisfying the spectral efficiency. Our simulation results show that our proposed caching scheme can effectively reduce the backhaul resources consumption.

With deployment of small cells, cellular multicast is gaining attractions as it can efficiently deliver large amounts of multimedia data to cellular users. Evolved Multimedia Broadcast Multicast Service (eMBMS) is an efficient broadcast/multicast mechanism to deliver shared content from LTE network to multiple users. It introduces Multimedia Broadcast multicast service Single Frequency Network (MBSFN) for the video sharing among users to guarantee the Quality of Experience (QoE) of users. In MBSFN, each Small cell Base Station (SBS) should determine the appropriate Modulation and Coding Scheme (MCS) for each physical Resource Block (RB), so as to simultaneously maximize the multicast throughput and satisfy the users' experience requirements. However, existing works did not consider the effect of limited wireless backhaul capacity on the multicast throughput. In this paper, we first demonstrate that limited backhaul capacity would affect the system throughput. Then we formulate the problem of MBSFN-based multicast resource allocation with the constraint of limited wireless backhaul capacity. To solve the problem, we develop a near-optimal algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is important when identifying the optimal multicast resource allocation solution.

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.

In energy-constrained Low Earth Orbit (LEO) satellite constellations, in order to prolong the network lifetime, more traffic should be carried by the satellites with high battery level, which, in turn, may result in congestion in such satellites. To strike a balance, we study the multi-path routing problem which aims at Maximizing network Lifetime while maintaining a Guaranteed network Congestion level (MLGC). Particularly, we formulate such a problem as a linear programming. However, it is time-consuming that solving the proposed MLGC needs to joint multiple time intervals. Therefore, we further design an Energy Aware Multi-path Routing (EAMR) strategy without solving the optimization problem. Simulation results show that the performance of EAMR is comparable with MLGC and moreover, compared with available routing strategies, the network lifetime can be effectively improved while the required congestion level being guaranteed by implementing our proposed schemes.

Hybrid wireless networks are networks that are composed of both ad hoc transmissions and cellular transmissions. Many existing works have analyzed the capacity of hybrid wireless networks. By assuming the uniform traffic model that a source node would select a random node as the destination, the network capacity is a function of number of nodes and number of base stations. Nevertheless, the real network traffic pattern is related to the social behaviors of users. In this work, we study the capacity of hybrid wireless networks with the social traffic model under the L-maximum-hop routing policy. If two nodes are within L hops away, packets will be transmitted in the ad hoc mode; otherwise, packets are transmitted through the base stations. To our best knowledge, we are the first to study this problem and develop the capacity as a function of number of nodes, number of stations, traffic model parameters, and L.

An important consideration for LEO satellite networks is choosing suitable routes to prolong the network lifetime while stringently guarantee the throughput requirement. However, both the highly dynamic network topology and intrinsically time-varying renewable energy availability pose great constraints and challenges in designing such routing schemes. To solve the problem, we resort to Capacity Region Evolving Graph (CREG) and formulate the throughput constrained maximum lifetime routing problem. Unfortunately, solving the problem without exploiting its special structure is indeed time-consuming, since multiple time intervals must be jointly handled. Two efficient routing algorithms, namely, Maximum Lifetime Routing (MLR) and Shortest Path-based Progressive Routing (SPPR), are thus proposed to reduce the execution time of solving the routing problem. Specifically, MLR decomposes the problem into multiple independent subproblems without trading its optimality, while SPPR exploits the deterministic mobility of satellite networks without solving the optimization problem. Simulation results verify that prolonged network lifetime and balanced traffic distribution can be obtained for both the routing algorithms.

In this paper, we investigate the capacity of two- layered satellite networks. Particularly, we propose a unified mathematical framework to formulate the relationship between network capacity and architectural parameters. Then we study the capacity of three typical scenarios. The analytical solutions show that the capacity of individual layer increases linearly with the link bandwidth of that layer. It also increases when there are more orbits and more satellites in each orbit. Moreover, when each LEO satellite can only connect to the nearest MEO satellite, the network capacity is approximately equal to the total capacity of the two layers, and is independent with the architectural parameters such as altitude of both layers and elevation angle of LEO satellites. When each LEO satellite is allowed to connect to all the MEO satellite in its coverage, the network capacity can be further increased. As the coverage size is impacted by the architectural parameters, the network capacity in this case is non-decreasing with the altitude of the MEO layer, and is non-increasing with the altitude of the LEO layer and the elevation angle of the LEO satellites.

In this paper, we study the propagation of deadline- constrained content over wireless cellular network, in which the base station transmits a content to a certain set of users. The lifetime of a content is consumed in two aspects: waiting in the queue and transmitting. In our system, the base station first transmits the content to users with a certain data rate, such that some users may not directly receive the content. Afterwards, the users who obtained the content would forward the content to the other users. Due to the deadline constraint of each content, we formulate the scheduling problem by using Markov Decision Processing (MDP) with the objective of maximizing the throughput of the whole system. We propose an algorithm based on value iteration. Extensive simulation results are provided to demonstrate that our scheduling algorithm can efficiently improve system throughput.

This paper studies the interference management of D2D communications in cellular networks. We consider the underlay D2D communication, such that the signal quality of cellular user would be affected by D2D users. We explore the application of network coding to mitigate interference. In our proposed interference management scheme, the helper nodes overhears the signals from cellular users, and then, code the received packets and sends to the base station. We design the transmission policy for the helper node, and also describe how to select the helper nodes. Our simulation results show that the proposed interference management can effectively improve the spectrum efficiency and increase system throughput.

A Multilayered Satellite Network (MLSN) tends to be a promising architecture in facilitating global ubiquitous broadband communication. However, unbalanced traffic distribution among its satellite layers should frequently occur, where the lower layers could get relatively congested while the upper layers remain underutilized. This unfair distribution of network traffic can lead to large end-to-end delay and severe throughput degradation. To cope with the above issue, we propose a Tailored Load-Aware Routing (TLAR) strategy to optimally distribute traffic load among the multiple satellite layers, so that the overall traffic congestion in the MLSN is minimized. In TLAR, an optimal portion of network load, which is decided based upon the newly arrived traffic estimation and theoretical analysis of traffic congestion rate in each layer, is detoured through the upper layer. The performance of the proposed routing method has been validated through extensive simulations, which demonstrate that TLAR can significantly alleviate traffic congestion, achieve low end-to-end delay and sustain improved throughput.

The quality of service (QoS) performance analysis is of great guiding significance for a QoS guarantee of secondary users (SUs). However, the QoS performance of SUs has not been well studied, especially how it is impacted by the traffic property of primary users (PUs). In this study, the authors propose a method to analyse the queue performance of the SU when the active and inactive durations of PUs follow general distributions. To characterise the non‐memoryless property of the channel when the distributions of PUs active/inactive durations are general distributions, they propose a two‐dimensional Markov chain to model the states of the channel. By using this Markov chain, they derive the effective capacity (EC) function of the cognitive radio network. On the basis of the EC function and the effective bandwidth function, the queue performance of the SU, that is, the stationary tail distribution of the queue length, is estimated. The author's result can not only be used to calculate other QoS metrics, such as the buffer overflow probability and throughput, but also provide some guidelines for QoS guarantee of the SU. Finally, they verify their work by comparing the analytical results with simulation results.

Successive Interference Cancellation (SIC) is a potentially powerful technique for improving the performance of multi-hop wireless networks, owing to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. In this paper, we address the problem of finding the route with maximal end-to-end spectral efficiency in multi-hop wireless networks, under the constraint of optimal bandwidth sharing. By taking advantage of SIC, more transmission opportunities are exploited by the nodes along the selected path. We formulate a cross-layer optimization framework to quantify the spectral efficiency improvement with SIC and then make use of several structural properties to derive exact solutions. Additionally, three SIC-based routing alternatives with low computational complexity are proposed, on the basis of the conventional shortest path algorithm, to obtain spectrum-efficient routes. Numerous simulation results verify that SIC can bring significant gains in terms of spectral efficiency.

Precomputation of the supported QoS is very important for internet routing. By constructing routing tables before a request arrives, a packet can be forwarded with a simple table lookup. When the QoS information is provided, a node can immediately know whether a certain request can be supported without launching the path finding process. Unfortunately, as the problem of finding a route satisfying two additive constraints is NP-complete, the supported QoS information can only be approximated using a polynomial time mechanism. A good approximation scheme should reduce the error in estimating the actual supported QoS. Nevertheless, existing approaches which determine this error may not truly reflect the performance on admission control, meaning whether a request can be correctly classified as feasible or infeasible. In this paper, we propose using a novel metric, known as distortion area , to evaluate the performance of precomputing the supported QoS. We then analyze the performance of the class of algorithms that approximate the supported QoS through discretizing link metrics. We demonstrate how the performance of these schemes can be enhanced without increasing complexity. Our results serve as a guideline on developing discretization-based approximation algorithms.

Admission control is important in achieving QoS guarantees in multi-hop wireless networks. An efficient admission control algorithm requires an accurate estimation of the end-to-end delay distribution of the network. In this paper, we propose a method to estimate the end-to-end delay distribution under the general traffic arrival process and Nakagami-m channel model. We firstly propose a novel two-dimensional Markov Chain to model the node behaviors in a multi-hop multi-rate IEEE 802.11 network that is subject to interference and error prone channel. By combining the basic Probability theory and Network Calculus, we analyze the delay a packet experiences at each hop along a path. The per-hop delay result is used to develop the distribution of the end-to-end delay of a randomly chosen path. We then develop an admission control scheme for the traffic with stochastic QoS guarantees. Finally, through simulation results, we verify the accuracy of our analytical model and the effectiveness of the proposed algorithm.

In smart grid, the scale of pole devices that monitor the health of power line is very large. Moreover, with the upgrade of smart grid, the number of these resource-constrained (in terms of memory and computation) devices is further increasing. These devices are easy targets to security attacks as they are accessible via wireless network, and use weak passwords for authentication and transferring telemetric data to the pole maintenance personnel. In this paper, we present a SCalable and Automated PAssword- CHanging protocol, SCAPACH, for unique authentication of human personnel (operator) and secure collection of telemetric data from a large number of pole devices. SCAPACH employs physical per- operator, per-pole-device information as well as changeable secret salts to generate new unique passwords and secret keys every time a pole device is accessed. Our experiments confirm that the password-changing protocol authenticates and transmits pole device data securely and in real-time under varying maintenance scenarios.

Successive Interference Cancellation (SIC) is a new physical layer technique which enables the receiver to either partially cancel the interfering signals or receive more than one desired signal at a time. By fully exploring the potential advantages of SIC, we develop an SIC Aware Routing protocol, referred to as SAR, aiming at enhancing the overall end-to-end throughput. An SICable condition is defined, by which our routing protocol can discover the links with potential SIC opportunities to improve the overall throughput. By using the concepts of spatial resource consumption and bandwidth efficiency, we characterize the benefits of SIC effectively. Based on the concepts, we design an SIC aware routing metric to discover the paths with high throughput and less spatial resource consumption. Simulation results show that our routing protocol achieves significant gains in network throughput and SIC ratio compared with minimum hop count routing and conventional interference aware routing.

End-to-end delay analysis is an important element of network performance analysis in multi-hop wireless networks. In this paper, we analyze the end-to-end delay of wireless networks with general traffic model and capacity-varying channel. A new concept of residual effective capacity using Effective Bandwidth theory and Effective Capacity theory is presented, which allows us to calculate the cumulative distribution function of queuing delay. We derive a formula to calculate the average end-to-end delay for multi-hop wireless networks and validate our analysis through simulations.

Network coding is considered as a promising technique to increase the bandwidth available in a wireless network. Many studies show that network coding can improve flow throughput only if an appropriate routing algorithm is used to identify paths with coding opportunities. Nevertheless, a good routing mechanism is very difficult to develop. Existing solutions either do not estimate the path bandwidth precisely enough or cannot identify the best path in some situations. In this paper, we describe our coding-aware routing protocol that provides a better path bandwidth estimate and is able to identify high throughput paths. Extensive NS2 simulations show that our protocol outperforms existing mechanisms.

Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths.

Network coding is known to be a promising technology to increase the bandwidth capacity in wireless networks. To our best knowledge, there is limited work on studying the available bandwidth for a given path with network coding. This paper presents a new method to estimate the available bandwidth of a path that considers network coding and wireless interference simultaneously. We show that our estimated path bandwidth can be easily achieved using a simple scheduling scheme. We also show that path bandwidth estimation is more accurate than other existing method through theoretical analysis and simulation experiments.

In this paper, we propose a new path metric for finding the maximum available bandwidth path in the multi radio multi-channel wireless mesh networks. We formally prove that the path metric is isotonic, which is the necessary and sufficient condition for assuring the proper operation of the routing algorithm. Based on the metric, we develop a routing protocol which jointly considers the path selection and the channel assignment. The time complexity of our routing algorithm is polynomial. We conduct the simulation experiments to compare the proposed metric with the existing metrics for finding the maximum available bandwidth path.

This paper discusses the problem of computing the maximum available bandwidth of a given path in TDMA-based network with network coding, which is a fundamental issue for supporting QoS with bandwidth requirement in wireless networks. We present a new path bandwidth computation mechanism considering physical-layer network coding. To our best knowledge, our work is the first proposal on assigning time slots with consideration of wireless interference and network coding simultaneously. Our simulation experiments show that our approach produces higher throughput than the existing approach.

In a wireless network that supports multiple flows, allocation of bandwidth resource among the flows is one of the critical problems. Different allocation strategies have been developed based on different optimization objectives. Unfortunately, these objectives may not reflect directly the time needed for a flow to transmit what it wants. In this paper, we define a new objective, average waiting time, that reflects the average time needed for the flows to finish their transmissions. For small networks, we develop an optimal scheme that minimizes the average waiting time. We extend the mechanism for general networks, and simulation results show that it can significantly reduce the average waiting time when compared with other existing mechanisms.

With the rapid development of the mobile device technology and wireless network technology, the need of an efficient file sharing method on wireless network becomes more and more significant. Peer-to-Peer(P2P) file distribution, as a quite popular method being used now, is a promising choice. However, the limitation of bandwidth of wireless networks greatly restricts the performance of wireless P2P. In this paper, we propose a new idea of better utilizing the limited bandwidth to improve the file distribution performance. The criteria of an optimal splitting of the half-duplex bandwidth is deduced with mathematical analysis. To achieve a further improvement on the average distribution time, we also propose a grouping strategy which works with the bandwidth strategy. Simulation results show that our mechanism can efficiently reduce the file distribution time among wireless peers.

Peer-to-Peer (P2P) file distribution has been widely used for file sharing in recent years. When compared with the traditional client-server model, the P2P model is a lot more efficient as each user can act as both a client and a server. This enables the P2P file distribution to scale well with increasing number of users. Grouping strategy has been introduced to reduce the average distribution time among peers without prolonging the total time needed to obtain a file. In this paper, a novel grouping strategy which groups peers of similar bandwidth together is introduced. We mathematically illustrate that under certain circumstances, this new grouping strategy performs better than the Greedy Grouping mechanism. To understand the performance of our grouping mechanism more comprehensively, we conduct extensive simulations. The results show that our mechanism can enhance the performance significantly in different network settings.

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead.

Many sensor network applications require the tracking and the surveillance of target objects. However, in current research, many studies have assumed that a target object can be sufficiently monitored by a single sensor. This assumption is invalid in some situations, especially, when the target object is so large that a single sensor can only monitor a certain portion of it. In this case, several sensors are required to ensure a 360° coverage of the target. To minimize the amount of energy required to cover the target, the minimum set of sensors should be identified. Centralized algorithms are not suitable for sensor applications. In this paper, we describe our novel distributed algorithm for finding the minimum cover. Our algorithm requires fewer messages than earlier mechanisms and we provide a formal proof of correctness and time of convergence. We further demonstrate our performance improvement through extensive simulations.

We consider multi-channel multi-interface wireless mesh networks with a schedule-based MAC protocol, where conflict-free transmission is ensured by requiring links assigned with the same channel and within the mutual interference range of each other to be active at different time slots. When a (point-to- multipoint) multicast call arrives, the call is accepted if a multicast distribution tree can be established for connecting the source node with all the receiving nodes, and with sufficient bandwidth reserved on each link. Otherwise, the call is rejected. To maximize the call acceptance rate, the multicast tree must be constructed judiciously upon each call arrival. Aiming at minimizing the carried load on the most-heavily loaded channel, and maximizing the residual capacity of the most heavily loaded node, an integer linear program (ILP) is formulated for multicast tree construction. Since solving ILP can be time-consuming, an efficient heuristic algorithm is then proposed. We compare the two tree construction algorithms by simulations. We found that both algorithms give comparable call acceptance rate, but the heuristic algorithm requires much shorter running time.

In this paper, we study the problem of computing the supported QoS from a source to a destination with multiple additive constraints. The problem has been shown to be NP-complete and many approximation algorithms have been developed. We propose a new approximation algorithm called multi-dimensional relaxation algorithm. We formally prove that our algorithm produces smaller approximation error than the existing algorithms. We further verify the performance by extensive simulations.

Finding the most likely path satisfying a requested additive Quality-of-Service (QoS) value, such as delay, when link metrics are defined as random variables by known probability distributions is NP-Hard. We transform the probability distributions into the Laplace domain, find the Laplace Transform of their convolutions and numerically inverse to find the distribution function in the time domain. Picard's iterative method of successive approximations is used to find the solution. To the best of our knowledge, ours is the first to propose a transform-based approach for the QoS routing problem of finding the most likely path. Simulations show that our stochastic approach (1) Selects correct paths more frequently, (2) Incurs less overhead with respect to the dissemination and processing of state information, and (3) Reduces the churn by selecting more stable paths.

Random walk (RW) has been widely used as a strategy for searching in peer-to-peer networks. The boom of social network applications introduces new impact to the classical algorithms on the Internet. In this paper, we model the random walk algorithm in peer-to-peer networks when social information is available. We define the social relationship between two nodes as the knowledge about the resources the other node possesses. We mathematically show that the social information can benefit the searching by extending the existing random walk search model.

In this paper, we consider the problem of routing with two additive constraints in the hierarchical networks, such as the Internet. In order for scalability, the supported QoS information in the hierarchical networks has to be aggregated. We propose a novel method for aggregating the QoS information. To the best of our knowledge, our approach is the first study to use the area-minimization optimization, the de facto optimization problem of the QoS information aggregation. We use a set of real numbers to approximate the supported QoS between different domains. The size of the set is predefined so that advertisement overhead and the space requirement will not grow exponentially as the network size grows. The simulation results show that the proposed method outperforms the existing methods.

This paper presents a distributed polynomial algorithm for finding the maximum bandwidth path in Wireless Mesh Networks (WMNs). Our proposed algorithm can be applied for designing the proactive hop-by-hop routing protocol with bandwidth guarantee. To the best of our knowledge, our work is the first distributed path calculation algorithm in WMNs.

Widest spanning tree is a broadcast tree with its bottleneck link bandwidth maximized. It provides a cost effective broadcasting solution in multi-channel multi-interface wireless mesh networks. To find the widest spanning tree, existing algorithms jointly consider channel assignment, routing and scheduling while assuming the number of network interface cards (NICs) at each node is given. In this paper, we treat the number of NICs at each node as a design parameter, whereas the total number of NICs in the system is given. By properly placing more NICs to more "critical" nodes, the bandwidth of the spanning tree can be further increased. To this end, a new integer linear programming (ILP) is formulated for solving the widest spanning tree problem based on joint optimization of interface placement, channel assignment, routing and scheduling. Numerical results show that interface placement provides a significant boost to the bandwidth of the widest spanning tree found.

In a wireless sensor network (WSN), when an adversary physically captures one or more sensor nodes, all the information stored on these nodes may be exposed completely. Consequently, the adversary can use the information to attack the remaining part of the network. In this paper, we investigate the effects of different node capture attack patterns on state-of- the-art key management schemes. We find that a compromised WSN can be made resilient to such attacks by introducing new resources, such as new nodes and new keys. Based on this observation, we propose two recovering strategies, namely, link replacement strategy and node replenishment strategy, to replace the compromised links and the functions of the compromised region, respectively. Simulation results indicate that our proposed strategies can improve the network resilience of a compromised WSN significantly with a small amount of additional resources.

It has been shown that the peer-to-peer paradigm is more efficient than the traditional client-server model for file sharing among a large number of users. Given a group of leechers who wants to download a single file and a group of seeds who possesses the whole file, the minimum time needed for distributing the file to all users can be calculated based on their bandwidth availabilities. A scheduling algorithm has been developed so that every leecher can obtain the file within this minimum time. Unfortunately, this mechanism is not optimal with regard to the average download time among the peers. In this paper, we study how to reduce the average download time without prolonging the time needed for all leechers to obtain the file from a theoretical perspective. Based on the bandwidth capacities, the seeds and leechers are divided into different groups. We identify the necessary conditions for grouping to bring about benefits. We also study the impact on performance when leechers leave the system before the downloading process is complete. To evaluate our mechanism, we conduct extensive simulations and compare the performance with a BitTorrentlike file sharing algorithm. The results show that our grouping protocol successfully reduces the average download time over a wide range of system configurations.

Recently, a time synchronization algorithm called pairwise broadcast synchronization (PBS) is proposed. With PBS, a sensor can be synchronized by overhearing synchronization packet exchange among its neighbouring sensors without sending out any packet itself. In an one-hop sensor network where every node is a neighbour of each other, a single PBS message exchange between two nodes would facilitate all nodes to synchronize. However, in a multi-hop sensor network, PBS message exchanges in several node pairs are needed in order to achieve network-wide synchronization. To reduce the number of message exchanges, these node pairs should be carefully chosen. In this paper, we investigate how to choose these ldquoappropriaterdquo sensors aiming at reducing the number of PBS message exchanges while allowing every node to synchronize. This selection problem is shown to be NP-complete, for which the greedy heuristic is a good polynomial-time approximation algorithm. Nevertheless, a centralized algorithm is not suitable for wireless sensor networks. Therefore, we develop a distributed heuristic algorithm allowing a sensor to determine how to synchronize itself based on its neighbourhood information only. The protocol is tested through extensive simulations. The simulation results reveal that the proposed protocol gives consistent performance under different conditions with its performance comparable to that of the centralized algorithm.

The capacity of an IEEE 802.11-based multi-hop wireless network is limited. By effectively utilizing multiple non-overlapping channels and multiple interfaces, collision and co-channel interference can be reduced. This allows more concurrent transmissions and thus enhances the network capacity. In this paper, we introduce an efficient distributed joint channel assignment and routing protocol, called J-CAR1. Unlike existing schemes, J-CAR allows a data interface to dynamically change its working mode between send and receive on a call-by-call basis, which enhances the utilization of both interface and channel. In J-CAR, channels are negotiated and assigned to active links in conjunction with the on-demand routing process. At each hop, J-CAR conducts a local optimization by selecting the least interfered channel according to the channel interference index. The channel interference index is designed by taking both the protocol and physical interference models into consideration. To find the least interfered path for network load balancing on a global scale, J-CAR employs a length-constrained widest-path routing. The “width” of a path is determined by the interference level of its bottleneck link. With an adjustable threshold on the path length (with respect to the shortest-path), the excessively long path can also be avoided. We show that with a comparable complexity as the existing schemes, J-CAR provides much higher system goodputs and shorter end-to-end packet delays.

Abstract In a sensor network that adopts data‐centric storage (DCS), information of the same kind is kept in the same set of nodes. That is, when a sensor detects a certain event, no matter where it is, it sends the information to the designated location. When another node wants the information, it can send a query to that location to retrieve it. Unfortunately, existing protocols based on DCS are prone to the hot‐spot problem where some nodes have to handle lots of messages. In this paper, we study how to apply diffuse caching on top of DCS. We diffuse popular information to other nodes so as to share the workloads. Our diffusing mechanism is adaptive that the distribution varies based on the popularity of the event type. We evaluate our protocol using simulations and the results show that our protocol successfully alleviates the hot‐spot problem and reduces the message overheads. Copyright © 2008 John Wiley & Sons, Ltd.

Many applications have been proposed in wireless sensor networks. For example, environmental monitoring, health monitoring, battlefield surveillance, etc. Most of these applications require the sensor nodes to transmit the up-to-date image or video back to the sink node for real-time processing. However, the computational power and energy constraints of the sensor devices greatly limit the possibility of using some traditional streaming approaches to stream the data back to the sink node. In this paper, we proposed a Group of Pictures (GOP) based geographic multipath routing scheme with selective retransmission in wireless sensor networks. In the routing algorithm, each intermediate sensor node will consider the effect of the drop of each frame to the video quality. As a result, the sensor nodes can conserve energy and maintain the predefined acceptable video quality by only selectively retransmit the frames which are more important to the video quality. An extensive simulation has been performed and the results show that our scheme can more evenly distribute the loads to different sensor nodes in the network without sacrificing much in video quality.

We study in this paper the scheduling problem in P2P file distribution. Our aim is to reduce the average distribution time. We present two distribution mechanisms: distributing the rarest pieces first and distributing to the least demanding nodes first. The new algorithm, rarest-piece-first and most-demanding-node-last-piece-oriented, is developed and we demonstrate by simulation its effectiveness over some related algorithms.

With the enhancement in channel bandwidth and mobile devices, more broadcast applications will be deployed in the wireless mesh networks (WMNs). While traditional approaches focus on finding a single broadcast tree in the network, we aim at maximizing the number of broadcast trees/calls that can be carried. In this paper, we first formulate an Integer Linear Program (ILP) for solving the minimum-channel-utilization broadcast tree problem in multi-channel multi-interface WMNs. In our ILP, channel assignment, routing, and scheduling are jointly considered for finding a broadcast tree that can minimize the maximum channel utilization. Intuitively, this balances all the accepted traffic load in the network, which in turn maximizes the chance of accepting future calls. However, solving ILP usually takes time and is less suitable for a system with real-time call arrival. An efficient heuristic algorithm is then designed. Our simulation results show that the proposed heuristic gives real-time response and provides comparable good performance as the ILP approach.

In the past few years, many new mobile technologies including the 3G, WiFi or mobileTV have created unprecedented learning opportunities on mobile devices. Furthermore, such technologies continuously fuel the rapid growth of new fields of research like the edutainment for educational entertainment. In a recent project awarded by the Hong Kong Wireless Development Center, we have developed a mobile quiz game system on 3G mobile phone networks in China, Hong Kong or other countries to facilitate learning anytime and anywhere. Our developed mobile quiz system is so generic that it can be readily extended to any wireless network. In this paper, we discuss about the design and possible uses of our quiz system in mobile learning, and also share the relevant experience in system development with the evaluation strategies carefully examined. After all, our work shed light on many interesting directions for future exploration.

In this paper, a distributed network-wise synchronization protocol is presented. The protocol employs Pairwise Broadcast Synchronization (PBS) in which sensors can be synchronized by merely overhearing the exchange of synchronization packets. We investigate how to minimize the number of PBS required to synchronize all nodes in a network. We show that the problem of finding the minimum number of PBS required is NP-complete. A distributed greedy algorithm is proposed. The protocol is tested by extensive simulations. Although the algorithm behind is heuristic-based, the performance is closed to the centralized algorithm. The message overhead is compared with that of Timing-Sync Protocol for Sensor Networks (TPSN).

Routing is a process of finding a network path from a source node to a destination node. A good routing protocol should find the "best path" from a source to a destination. When there are independent constraints to be considered, the "best path" is not well-defined. In our previous work, we developed a line segment representation for Quality-of-Service routing with bandwidth and delay requirements. In this paper, we propose how to adopt the line segment when a request has two concave constraints. We have developed a series of operations for constructing routing tables under the distance-vector protocol. We evaluate the performance through extensive simulations.

Peer-to-peer networks leverage the upload bandwidth of leechers, which results in a significant improvement of scalability over that of client-server networks. Numerous P2P applications serve as overlay networks for file distribution. In evaluating the performance of such systems, file distribution time is an important metric. Based on fluid models, scheduling algorithms that allow files to be downloaded in a minimum time have been developed. To further improve the system performance, our objective is to reduce the leechers' average download time while maintaining the minimum download time. A grouping scheme is presented based on the bandwidth characteristics of the network. According to this optimization objective, we identify cases where it is beneficial to apply the grouping strategy. Simulation results show that applying grouping schemes in suitable cases brings in significant performance improvement over a wide range of networks of varying bandwidth characteristics.

The problem of finding a path that satisfies two additive constraints, such as delay and cost, has been proved to be NP-complete. Many heuristic and approximation algorithms have been developed to identify a path given a certain QoS request. Unfortunately, these algorithms cannot be applied directly in the Internet because routing in the Internet is based on table lookups and routing tables are computed before a request arrives. In this paper, we develop an approximation algorithm for computing the supported QoS going across a domain. We analyze the approximation error of our algorithm and formally prove that the approximation error of our proposed algorithm is smaller than those of the existing approaches. We further verify our performance using extensive simulations.

Network applications that are in need of some level of guarantees from the network to operate, such as multimedia programs, are not well served with the conventional best-effort service of the IP-based networks. The difficulty of finding preferential paths for those applications is compounded by the intrinsic inaccuracies of the network state information maintained by the nodes that have to make such decisions. We use a probabilistic modeling and a framework to select paths for applications that want more cooperation from the network to operate satisfactorily. The links are associated with additive link metrics. We represent the stochasticity of links by means of a new composite metric composed of an interval with a lower and upper bound and an associated probability. The interpretation and relevance of our metric is such that in the next decision time period the expected value of the resource is between the upper and the lower bound with the associated probability. Three simple and straightforward methods of computing our composite metric are presented. An algorithm, called Augmented-Dijkstra Additive Metric (ADAM), with the same complexity as the standard Dijkstra algorithm, provides an effective solution for statistical additive link metric (such as delay) guarantees. Simulation results conducted in ns2 evaluate and confirm the effectiveness of our approach.

Efficient broadcast schemes are essential in wireless mesh networks (WMNs) for minimizing the content update time. In this paper, we consider the widest spanning tree problem in a multi-channel multi-interface WMN, where the width of a tree is determined by the bottleneck link bandwidth. To the best of our knowledge, we present the first effort in solving the widest spanning tree problem using mathematical formulation. In our model, we jointly consider and solve the problems of channel assignment, routing, scheduling and server/root placement. Unlike other spanning tree approaches, we allow WMN nodes to have heterogeneous number of network interface cards (NICs), and multiple NICs of a node can share the same assigned set of channels. To find a practical schedule, we also introduce the channel conflict graph and NIC constraint graph, and show that the associated scheduling problem is equivalent to the classic graph coloring problem.

Wireless sensor networks are useful for many real-world applications including environmental monitoring, military applications, disaster management, etc. In many cases, interesting events detected by sensors are disseminated to some targeted node(s) for storage whereas queries for specific event types would be directed by a data dessimination protocol aiming to the "right" storage node(s) for definite answers. Nevertheless, the query/event ratio can be changing over time in many practical applications like the tracking of endangered animals in an open safari due to the seasonal trend or other factors. This varying query/event ratio will significantly affect the overall performance of different data dissemination schemes in the underlying sensor networks. In this paper, we consider an adaptive framework that can flexibly switch from one scheme to another based on the results quickly evaluated by our cost models. We implemented several GHT-based schemes including our adaptive GHT (AGHT) for event distribution in the JSim packages, and compared their performance on an example safari application with changing query/event ratios. In our simulation results, the AGHT clearly excelled the other GHT-based schemes with the lowest storage and communication overheads. More importantly, these promising results shed light on many possible directions for future investigation. © 2007 IEEE.

In multi-channel multi-interface mobile ad hoc networks (MANETs), channel assignment and routing can be conducted jointly to improve network capacity. In this paper, we first extend an existing joint channel assignment and routing scheme (J-CAR) to support bidirectional path setup. Compared with unidirectional path setup schemes, the amount of broadcast control traffic and path setup delay is roughly halved. Then, a new channel interference index is designed to facilitate channel selection at each hop. Since both distance and the number of interfering sources are considered, the new interference index allows channels with better quality to be selected first. To further improve network capacity, the loading in the network should be balanced. To this end, a new length-constrained widest-path routing algorithm is designed, where the "width" of a path is determined by the interference level of its bottleneck link. With an adjustable threshold on the path length (with respect to the shortest path), the excessively long path can also be avoided. Simulation results show that, due to the improved load balancing and channel selection performance, our new joint channel assignment and routing algorithm (J-CAR/widest) outperforms the existing J-CAR and its two variants (J-CAR+index and J-CAR+widest) by delivering higher system goodputs and lower end-to-end packet delays.

In this paper, we study the angle coverage problem in visual sensor networks where all sensors are equipped with cameras. An object of interest moves around the network and the sensors near the object are responsible for capturing images of it. The angle coverage problem aims to identify a set of sensors that preserve all the angles of view of the object while fulfilling the image resolution requirement. The user is required to specify the minimum acceptable image resolution in the request. Only the images that fulfill the resolution requirement will be considered. In order to save transmission energy, the number of images to be sent should be minimized. We develop a distributed algorithm to identify the minimum set of sensors such that all these images cover the maximum angle of view of the target. Our simulation results show that our protocol can achieve significant reduction in transmission load while preserving the widest angle of view.

The notion of p-cycle (preconfigured protection cycle) allows capacity efficient schemes to be designed for fast span protection in WDM mesh networks. Conventional p-cycle construction algorithms need to enumerate/pre-select candidate cycles before ILP (integer linear program) can be applied. In this paper, we propose a new algorithm which is only based on ILP. When the required number of p-cycles is not too large, our ILP can generate optimal/suboptimal solutions in reasonable amount of running time.

Wireless sensor networks are under active research for tracking systems, where camera nodes are installed in a large area to take images of a targeted object. In this paper, we consider the scenario where the sensors around the object of interest capture images of it. Since some sensors are in similar viewing directions, the images they capture likely exhibit certain levels of correlation among themselves. It is a waste of transmission energy if we blindly send all images to the sink without checking for redundancy. We develop a protocol for involved sensors to determine how to select and transmit the images to the mobile sink in an energy efficient manner. The simulation results show that our protocol can achieve a significant reduction in energy consumption while preserving most of the viewing directions

Many localization algorithms have been proposed in recent years. Although different algorithms based on different methodologies, the use of anchors is common to most algorithms. The placement and the density of anchors affect the accuracy of different algorithms to different extent. Location estimates are usually more accurate with a higher density of anchors. When there are only a few anchors, efficient algorithms tend to perform poorly. However, having more anchors will increase the cost of a sensor network. In this paper, we present an algorithm which uses two different localization techniques, multidimensional scaling (MDS) and proximity-distance map (PDM), in a phased approach. MDS has a high complexity but can give good results when there are only very few anchors. PDM, on the other hand, is a distributed algorithm but performs poorly when anchors are scarce. The phased approach has comparable complexity to PDM but less than MDS. With extensive simulations, we demonstrate that the proposed algorithm gives accurate solution with very few anchors or clustered anchors which is intrinsically a difficult challenge to most existing algorithms.

In this paper, we study the angle coverage problem in visual sensor network. We consider a tracking system where an object of interest moves around the network, and the sensors surrounding it are responsible for capturing the images of it. We aim at finding the minimum cost cover which preserves all the angles of view with minimum transmission cost. We proved formally that the minimum cost cover problem can be transformed into the shortest path problem. Due to the acyclic nature of graphs generated in the transformation, we can develop a distributed algorithm to solve the problem, which is a lot more efficient than the distance vector protocol. Our simulation results show that our algorithm can successfully save a lot of energy.

Devices in a sensor network need to work in a hostile environment and they are usually powered by batteries. Yet the whole purpose of deploying a sensor network is to perform distributed collaborative computing, possibly in a massive scale. In a hostile computing environment, the sensor devices might be routinely tampered with. Together with the possibility of faulty devices due to extreme conditions or low power, the trustworthiness of a device varies. Specifically, a device should only communicate with another device which has a trust level above a certain threshold. However, setting up trusted communication channels among sensor devices remains a major challenge. In this paper, we propose a trust-based routing scheme in sensor networks for providing a high level of robustness in node selection based on packet trust requirement with lifetime consideration. Our protocol allows messages to be routed through malicious and faulty devices with the selection of trusted neighbors. On the other hand, the network lifetime can also be prolonged by selecting those with their sensing functions covered by some existing nodes. Simulation results show that our scheme is possible to prolong the lifetime of sensor networks and maintain certain satisfactory delivery ratio.

Peer-to-Peer file sharing applications in the Internet, such as BitTorrent, Gnutella, etc., have been immensely popular. Prior research mainly focuses on peer and content discovery, overlay topology formation, fairness and incentive issues, etc. However, little attention has been paid to investigate the data distribution problem which is also a core component of any file sharing application. In this paper, we present the first effort in addressing this collaborative file distribution problem and formally define the scheduling problem in a simplified context. We develop several algorithms to solve the problem and study their performance. We deduce a theoretical bound on the minimum download time experienced by users and also perform simulations to evaluate our algorithms. Simulation results show that our graph-based dynamically weighted maximum-flow algorithm outperforms all other algorithms. Therefore, we believe our algorithm is a promising solution to be employed as the core scheduling module in P2P file sharing applications.

Computer games on mobile devices including cellular phones or handheld computers have become a fast expanding industry due to the recent advance in hardware and software supports. Most large mobile game and handheld vendors mainly focus on improving the interactivity and visual effects of, commonly single-user, mobile games. In this project, we carefully designed and then implemented an interactive multi-player action game, namely the BlueGame, transferrable between different computing platforms supported by the Bluetooth wireless technology. In addition to individual user's convenience to continue the multi-player game, our BlueGame prototype highlighted certain shortcomings of the existing Bluetooth technology, and more importantly our valuable experience gained for future wireless game development. ©2006 IEEE.

Wireless sensor networks are highly useful for many location-sensitive applications including environmental monitoring, military applications, disaster management, etc. Localization in wireless sensor networks concerns about the precise estimation of node positions given a relatively small portion as anchor nodes with their absolute positions predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance/path measures. Most of the existing work focus on increasing the accuracy in position estimation typically by using different heuristic-based or mathematical techniques. On the other hand, there were many complex optimization problems successfully tackled by the nature inspired search algorithms including the ant-based or genetic algorithms. In this paper, we propose to adapt an evolutionary approach, namely a microgenetic algorithm, and integrate as a post-optimizer into some existing localization techniques such as the Ad-hoc Positioning System (APS) to further improve their position estimation. Clearly, our proposed MGA is so adaptable that it can easily be integrated into other localization methods. More importantly, the remarkable improvements obtained by the prototype of our proposed evolutionary optimizer on certain anisotropic topologies of our simulation tests prompt for further investigation. © 2006 IEEE.

We propose an efficient joint channel assignment and routing protocol (J-CAR) for multi-channel multi-interface mobile ad hoc networks (MANETs). Aiming at overcoming the limitations of the existing channel assignment and routing algorithms, J-CAR negotiates a channel at each active link during the route setup process. It has the following major features: (a) a pre-determined common control channel is used by every node for routing and channel negotiation; (b) control packets for data transmission (RTS, CTS & ACK) are carried by the associated data channels; (c) the spare capacity on the control channel can be used for data transmission; (d) an interface is free to change its working modes between send and receive; and (e) an interface can tune to any data channels for data sending or receiving at the cost of switching overhead. With J-CAR, a more flexible assignment of interfaces, channels, and the working mode of each interface can be rendered. The performance gain brought by J-CAR is substantiated by extensive simulation results.

Not all network applications can make do with the best-effort service of the original Internet design. Many new applications, including multimedia and mission-critical ones, require a certain level of service from the network to operate properly. The difficulty of finding such preferential paths is compounded by the intrinsic inaccuracies of the network state information maintained by the nodes that have to make such decisions. We choose a stochastic framework to select paths for applications that want more cooperation from the network to operate satisfactorily. We represent the stochasticity of links by means of a new composite metric composed of an interval with a lower and upper bound and an associated probability. The interpretation and relevance of our metric is such that in the next decision time period the expected value of the resource is between the upper and the lower bound with the associated probability. Simple and straightforward methods of computing our composite metric are presented. An algorithm, called Augmented-Dijkstra, with the same complexity as the standard Dijkstra, provides an effective solution for statistical bandwidth guarantees. Simulation results evaluate and confirm the effectiveness of our approach.

Wireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most location sensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation

In many real-world applications including agricultural, meteorological, military applications, etc, localization techniques are widely used to estimate the geographic locations of sensor nodes based on the precision positions of a few anchors equipped with special hardware. Existing localization algorithms mainly try to improve their accuracy in position estimation by using various heuristic-based or mathematical techniques. Every node in the network follows the same technique to find its physical location. However, each individual method with its own strength can only outperform the others in some but not all nodes. Based on this observation, we develop a hybrid approach for the localization problem. Each node collects the same kind of information. By analysing the information, a node can decide what is the best localization algorithm to use. Different nodes can make their own decisions. Our simulation results reveal that the hybrid approach is effective that it outperforms existing algorithms. To the best of our knowledge, our work presents the first effort in solving the absolute localization problem by adopting a hybrid approach

Peer-to-peer file sharing applications on the Internet, such as BitTorrent, Gnutella, etc., have been immensely popular prior research mainly focuses on peer and content discovery, overlay topology formation, fairness and incentive issues, etc, but seldom investigates the data distribution problem which is also a core component of any file sharing application. In this paper, we present the first effort in addressing this collaborative file distribution problem and formally define the scheduling problem in a simplified context. We suggest several types of algorithms, including a novel bipartite matching algorithm, for solving the problem. Simulation results show that our weighted bipartite algorithm finds an optimal solution for all cases tested. Therefore, we believe our algorithm is a promising solution to be employed as the core scheduling module in P2P file sharing applications, shortening the total download time experienced by users.

A sensor network consists of a large number of sensors which are equipped with sensing, computation, and communication devices. Due to limitation in size, a sensor has only limited energy and storage. Traditional wireless network protocols cannot be applied in sensor networks directly. We study the distribution of events and queries in sensor networks. An event is something of interest detected by a sensor. A query is a request of information. A conventional approach to facilitate query nodes to acquire what they want is flooding. Nevertheless, flooding is not desirable in sensor networks due to the large number of nodes and limited energy in sensors. Recently, the concept of data-centric storage (DCS) is introduced where information of the same kind is kept in the same set of nodes. Queries can then be sent to these nodes for information retrievals. Theoretical analysis shows that this approach requires a lot fewer messages than the flooding approach when query frequencies are not high. Unfortunately, existing protocols based on DCS are prone to the hot-spot problem where some nodes have to handle lots of messages. In this paper, we present an efficient protocol for distributing events and queries in a location-aware sensor networks so that the load among nodes is more evenly distributed. We evaluate our protocol using simulations and the results show that our protocol successfully alleviates the hot-spot problem.

A virtual private network (VPN) is a private data network where remote sites are connected over a shared provider network. In order to provide secure communications between customer sites, predetermined paths are used to forward data packets. To support quality of service (QoS), bandwidth has to be reserved on these paths. Then, finding appropriate paths in order to optimize the bandwidth used becomes an important problem. In this paper, we study the routing problem of VPNs under the hose model, where VPN endpoints specify the maximum bandwidth they need in sending and receiving data. Some previous works considered the problem under the assumption that all links have infinite capacities. We remove this constraint in our studies and develop enhancement to existing algorithms. Our simulation results show that our algorithm works very well in networks where link capacities are tight.

To enable end-to-end quality-of-service (QoS) guarantees in the Internet, based on the border gateway protocol (BGP), interdomain QoS information advertising, and routing are important. However, little research has been done in this area so far. Two major challenges, scalability and heterogeneity, make the QoS extension to BGP difficult. In the existing routing schemes, static and instantaneous QoS metrics, such as link capacity and available bandwidth, are used to represent QoS routing information, but neither of them can solve the two challenges well. In this paper, BGP is extended to advertise available bandwidth and delay information of routes, but, instead of using the traditional deterministic metrics, a series of statistical metrics, available bandwidth index (ABI), delay index (DI), available bandwidth histogram (ABH), and delay histogram (DH), are defined and applied to QoS information advertising and routing. Two major contributions of the proposed statistical metrics are: 1) QoS information is abstracted into one or several probability intervals and, thus, the heterogeneous and dynamic QoS information can be represented more flexibly and precisely and 2) by capturing the statistical property of the detailed distribution of QoS information, these new metrics are efficient and they can highly decrease the message overhead in routing, thereby making the QoS advertising and routing scalable. Our extensive simulations confirm both contributions of the QoS extension to BGP very well. Moreover, besides BGP, these statistical metrics can be applied to other networks and protocols to represent QoS information in a more scalable and precise way.

Routing is a process of finding a network path from a source node to a destination node. The execution time and the memory requirement of a routing algorithm increase with the size of the network. In order to deal with the scalability problem, large networks are often structured hierarchically by grouping nodes into different domains. The internal topology of each domain is then aggregated into a simple topology that reflects the cost of routing across that domain. This process is called topology aggregation. For delay-bandwidth sensitive networks, traditional approaches represent the property of each link in the aggregated topology as a delay-bandwidth pair, which corresponds to a point on the delay-bandwidth plane. Since each link after aggregation may be the abstraction of many physical paths, a single delay-bandwidth pair results in significant information loss. The major contribution of this paper is a novel quality-of-service (QoS) parameter representation with a new aggregation algorithm and a QoS-aware routing protocol. Our QoS representation captures the state information about the network with much greater accuracy than the existing algorithms. Our simulation results show that the new approach achieves very good performance in terms of delay deviation, success ratio, and crankback ratio.

When supporting source-specific heterogeneous-receiver multimedia applications, a multicast tree is built among a source and the receivers such that the path from the source to each receiver satisfies the delay and bandwidth constraints. To optimize the network usage, it is desirable to find a multicast tree that minimizes the total bandwidth used while satisfying the different delay and bandwidth requirements of the receivers. For scalability reasons, the desired protocol should require little or minimum storage in the sender and other on-tree routers. Moreover, to allow dynamic member joining or leaving, a receiver-initiated approach is more appropriate. We describe our receiver-initiated QoS multicast protocol that aims at reducing the bandwidth used in building a multicast tree for heterogeneous receivers by actively identifying better sub-optimal paths. Our protocol does not require additional information to be stored in the on-tree routers, and it is able to construct a better sub-optimal tree than existing protocols.

When supporting source-specific heterogeneous-receiver multimedia applications, a multicast tree is built among a source and the receivers such that the path from the source to each receiver satisfies the delay and bandwidth constraints. To optimize the network usage, it is desirable to find a multicast tree that minimizes the total bandwidth used while satisfying the different delay and bandwidth requirements of the receivers. For scalability reason, the desired protocol should require little or minimum storage in the sender and other on-tree routers. Moreover, to allow dynamic member join or leave, a receiver-initiated approach is more appropriate. In this paper, we describe our receiver-initiated QoS multicast protocol that aims at reducing the bandwidth used in building a multicast tree for heterogeneous receivers by actively identifying better sub-optimal paths. Our protocol does not require additional information to be stored in the on-tree routers, and it is able to construct a better sub-optimal tree than existing protocols.

This paper studies the problem of finding optimal routes for premium class traffic in the DiffServ network such that (1) loop-freedom is guaranteed in the entire network under hop-by-hop routing assumption; and (2) the maximum relative congestion among all links is minimized. This problem is called the extended optimal premium routing (eOPR) problem, which is proven to be NP-hard. We use the integer programming method to mathematically formulate the eOPR problem and find the optimal solutions for small scale networks. we also study heuristic algorithms in order to handle large scale networks. Simulation results are compared to handle large scale networks. Simulation results are compared with the optimal solutions obtained by solving the integer programming models. The results show that the bandwidth-inversion shortest path (BSP) algorithm can be a good candidate to route premium traffic in DiffServ networks.

With increasing demand for multimedia and real-time applications, local area network (LAN) technologies are rapidly being upgraded to support quality-of-service (QoS). Many QoS-enabled LANs are making use of resource allocation mechanisms that can discriminate among traffic classes of different priorities. When such LANs are interconnected by bridges to form an extended LAN, it is necessary to upgrade the bridges so that they are QoS-enabled as well. For example, the IEEE 802.1p standard defines a framework for priority queuing in bridges. Alternatively, frame forwarding decisions at the link later may be modified to recognize frame priorities and alternate paths may be used for differentiating QoS. In this paper, we describe a novel bridge protocol that can forward frames of different priorities using different paths. Our protocol ensures that the forwarding path of a higher priority frame is never longer than the forwarding path of a lower priority frame.

To enable the end-to-end quality of service (QoS) guarantees in the Internet, based on the border gateway protocol (BGP), inter-domain QoS advertising and routing are important. However, little research has been done in this area so far. Two major challenges, scalability and heterogeneity, make the QoS extension to BGP difficult. Two existing approaches, link capacity routing (LCR) and available bandwidth routing (ABR), address QoS advertising and routing in BGP with respect to the bandwidth metric, but neither of them can solve the two challenges well. We extend BGP to advertise bandwidth information, but, instead of using link capacities or instantaneous available bandwidth values, a novel QoS metric, available bandwidth index (ABI), is defined and used to perform bandwidth advertising and routing. The two major contributions of ABI are: (1) ABI dynamically abstracts available bandwidth into a probability interval, therefore, it is very flexible to represent heterogenous and dynamic bandwidth values; (2) by capturing the statistical property of the detailed available bandwidth distribution, ABI is so efficient that it can highly decrease the message overhead in routing, thereby making the QoS advertising and routing very scalable. Our extensive simulations confirm both contributions of the ABI extension to BGP very well.

Large networks are often structured hierarchically by grouping nodes into different domains in order to deal with the scaling problem. The internal topologies of the domains are aggregated before broadcasting and this process is called topology aggregation. We propose a new method of aggregating networks that are delay-bandwidth sensitive. Traditional approaches represent each logical link as a delay-bandwidth pair which is basically a point on a delay-bandwidth plane. We introduce a new QoS parameter representation and present an aggregation algorithm with corresponding routing protocol. Our simulation results show that the algorithm has very good performance in terms of success ratio and crankback ratio.

Large networks are often structured hierarchically by grouping nodes into different domains in order to deal with the scaling problem. In such networks, it is infeasible to maintain the detailed network information at every router. Therefore, the topology information of the domains are summarized before being broadcast. This process is called topology aggregation. Hierarchical routing protocols are then used to find a route among the domains. We study several basic problems associated with hierarchical QoS routing, including (1) how to make QoS-aware topology aggregation, (2) how to represent the aggregated network state, and (3) how to find an end-to-end route based on aggregated information. The novelty in this research is our new network QoS representation which is line segments on the delay-bandwidth plane. We also present a distributed routing mechanism that works with our representation. Our theoretical and simulation results show that the protocol achieves scalability and improved routing performance.

We develop an approach to deriving concrete engineering advice for cryptographic protocols from provable-security-style proofs of security. The approach is illustrated with a simple, yet useful protocol. Our main result provides the first published proof of an exact probabilistic relationship betwe en a high-level protocol and multiple cryptographic primitives. This exact relationship enables us to rigorously derive concrete recommendations on the bitlengths of cryptographic keys and on how often principals should rekey. As an additional benefit of our approach, the process of developing our theorem and proof lead us to identify and implement an improvement in our example protocol.

We develop an approach to deriving concrete engineering advice for cryptographic protocols from provable-security-style proofs of security. The approach is illustrated with a simple, yet useful protocol. The proof is novel and is the first published proof that provides an exact relationship between a high level protocol and multiple cryptographic primitives.