Intelligent radio maps (IRMs) have emerged as a critical enabler for next-generation wireless networks, offering comprehensive spatiotemporal awareness of the electromagnetic environment with limited sensing resources and low computational overhead. They play a crucial role in enhancing spectrum efficiency, enabling intelligent resource allocation, supporting anti-jamming communications, improving interference management, and facilitating environment-aware networking. This paper presents a systematic overview of how to construct high-quality IRMs. We first introduce six evaluation metrics aligned with practical deployment requirements and evolving wireless network demands. Guided by these metrics and recent advances in artificial intelligence (AI), we provide an in-depth review of spectrum sensing approaches and state-of-the-art methods for spectrum inference. We further explore the intrinsic connections between these two steps and propose an integrated sensing–inference construction scheme. Extensive experiments demonstrate that the integrated scheme achieves superior IRM construction performance under sparse sensing, validating its practical potential for future wireless networks.

In this paper, we study a UAV-Parking-Vehicles assisted Mobile Edge Computing (MEC) network for providing enhanced edge computing services, where a UAV serves to relay ground users’ tasks to parking vehicles having idle computing resources in the vicinity for processing.We formulate the problem of average task delay minimization in this case as a long-term discrete mixed integer programming problem. We transform this problem into two subproblems: Service deployment optimization problem at large time scale and task offloading optimization problem at small time scale. For the former, we define a system utility for measuring the effect of a service deployment profile at parking vehicles, formulate the system utility minimization problem for optimizing the service deployment at vehicles, and propose a Computing resource allocation and Genetic Algorithm based Service deployment Algorithm (CGSA) for obtaining optimized service deployment profiles at parking vehicles on a per cycle basis. For the latter, we propose a Bandwidth allocation, Task offloading, and Transmission scheduling Algorithm (BTTA) for determining optimized task offloading profiles for all users on a per time slot basis. Extensive simulation results show the high performance of our proposed algorithms compared with baseline algorithms.

Unmanned Aerial Vehicles (UAVs) enabled Mobile Edge Computing (MEC) has been an attractive paradigm for providing flexible and high-quality offloading services to ground users. In this paper, we study a hierarchical aerial MEC network architecture for improved quality of user experiences. Specifically, we study a bilevel UAV-enabled MEC network where a fixed-wing UAV (F-UAV) and multiple rotor UAVs (R-UAVs) are jointly deployed to provide continuous MEC services to ground users with dynamic demands. We formulate a long-term optimization problem for minimizing the utility of all users while considering the stability of task queue backlogs and energy consumption budgets at users and R-UAVs, where user utility measures the per-slot task processing performance at user side. We apply Lyapunov optimization technique to decompose the original problem into deterministic per-slot optimization subproblems. We derive the optimal offloading conditions at different types of edge nodes. We accordingly propose a Bilevel UAVs based Online Task processing and Resource allocation Algorithm (BOTRA) for determining the offloading and local processing profiles at users. Extensive simulation experiment results show the high performance of the proposed BOTRA algorithm compared with benchmark algorithms.

Task assignment is a key issue in mobile crowdsensing (MCS). Most existing work in this area has focused on the task assignment for the single platform scenario, which can cause considerable waste of limited human resources or reduced task completion rate due to potential spatial mismatching between distributions of users and tasks. In this article, we study multiplatform cooperative task assignment. The design goal is to maximize the social welfare while ensuring cooperative and individual rationality. We formulate this problem, transform it to a maximum value flow problem, and prove its NP-hardness. We first propose a greedy-maximum-flow-based task matching (GMTA) mechanism for interplatform task matching. In GMTA, there are two phases in each time slot: 1) in the former phase, earliest-deadline-first-based intraplatform optimal task assignment is carried out at each individual platform and 2) in the second phase, greedy-maximum-flow-based task matching is carried out to perform interplatform cooperative task assignment for those overloaded tasks in the first phase. We then enhance GMTA by designing an iterative-maximum-flow-based task matching (IMTA) mechanism, which is to achieve enhanced social welfare at the cost of increased computational overhead. We deduce time complexities of both mechanisms, and prove that they satisfy cooperative and individual rationality. Extensive simulations are conducted and the simulation results demonstrate the effectiveness of our proposed mechanisms.

Federated Learning (FL) enables collaborative model training across devices, but data exchanges pose privacy risks. Homomorphic Encryption (HE) is widely used to enhances privacy in FL but incurs significant communication and computation latency. Prior work reduced this latency using compressions, but sacrificed learning accuracy and overlooked the impact of the number of participating devices on latency. Over-the-air computation (AirComp) leverages wireless channels' superposition property to achieve high spectral efficiency and efficient aggregation irrespective of device number. In this paper, we propose HEAirFed, integrating AirComp with the state-ofthe-art HE scheme CKKS for efficient privacy-preserving FL. In HEAirFed, we develop a ciphertext-oriented wireless communication module to ensure homomorphic operations leverage AirComp's superposition property, enabling correct decryption. We further build a rigorous error analysis model, derive the worst-case upper bound of approximation error, and characterize this bound's impact on the convergence guarantee of HEAirFed, measured by the optimality gap with bounded approximation error. Then, we minimize this gap and derive a near-optimal solution in semi-closed form. Extensive experimental results on real-world datasets validate the ciphertext-oriented design's necessity, the error analysis's correctness, and demonstrate that HEAirFed achieves a substantial reduction in communication and aggregation latency compared to baseline, with minimal learning accuracy loss.

Mobile crowdsensing is a sensing paradigm using mobile users’ smart devices to perform sensing tasks, which has attracted much attention due to its low system cost, high flexibility, and wide coverage. In this paper, we study the hybrid sensing online task allocation problem for maximizing the total quality of completed tasks under given budget constraint. We formulate this problem as a 0-1 integer programming. To address this problem, we propose an efficient hybrid sensing based online task assignment algorithm (HSTA), which consists of two major components: Expected task completion quality based opportunistic user recruitment and participatory user recruiting and path planning. We present the detailed algorithm design of HSTA and deduce its computational complexity. Simulation results demonstrate the effectiveness of the proposed HSTA algorithm.

Task assignment is a critical issue in mobile crowd-sensing, which is aimed to maximize the number of completed tasks subject to budget constraints. However, existing work in this aspect did not consider the correlation between the tasks submitted by the same task requester. That is, tasks in the same subset from the same task requester are often correlated such that they are considered completed only when all of them are completed, and partial completion of them are useless. This requirement largely affects the performance of existing algorithms for the assignment of such partially correlated tasks. In this paper, we formulate the problem of maximizing the total number of completed tasks subject to such correlation and also budget constraints as an integer programming problem. We propose two greedy algorithms, one is requester happiness utility based algorithm and the other is minimum task remaining subset first algorithm. We present design details of both algorithms and deduce their computational complexities. Numerical results demonstrate that these two algorithms can significantly outperform the existing work.

Computing power network has emerged as an attractive technology to tackle the increasing demand for computational resources in cloud networking. In this paper, we study the problem of optimizing the formation of clusters of computing servers/nodes and also the task scheduling considering the games between the platform and computing nodes to maximize the platform profit. We formulate this problem as an integer programming problem. We propose a deep reinforcement-learning based server clustering and auction-based task scheduling algorithm working at different time scales to solve the problem. The deep reinforcement learning-based server clustering algorithm works at a large time scale to optimize the sizes and compositions of different clusters based on temporal and spatial distribution of the tasks and also their characteristics. The auction-based task scheduling algorithm works at a small time scale to match the tasks with the clusters while satisfying the QoS requirements of tasks so as to maximize the profit of the platform. Extensive simulations are conducted to evaluate the performance of proposed algorithm and the results show its high performance.

Mobile Edge Computing (MEC) can provide low-latency and workload-intensive computing services to user equipments. Elastic scaling, service placement, and task scheduling are key techniques affecting the performance of an MEC system. Elastic scaling is to determine the set of active servers and also the amount of computation resources allocated for each service deployed at a server, service deployment is to determine the set of services/applications to be deployed at each server, and task scheduling is to determine how tasks are assigned among different servers. In this paper, study an MEC system where user demands fluctuate spatially and temporally. Our objective is to minimize the total power consumption and task response time. We accordingly formulate the joint optimization of elastic scaling, service placement, and task scheduling in this case as a Mixed-Integer Nonlinear Programming (MINLP). Due to the hardness of the problem, we propose an efficient joint elastic scaling, service placement, and task scheduling algorithm. Simulation results show that our proposed algorithm can effectively reduce the system cost as compared with baseline algorithms.

Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.

Fixed-wing unmanned aerial vehicle (UAV) communication is a promising paradigm for providing mobile video services to ground users (GUs) without the need of infrastructure. However, the performance of fixed-wing UAV video streaming is severely limited by the onboard energy while energy-efficient fixed-wing UAV video streaming has not been fully explored. In this paper, we study energy-efficient video streaming with a fixed-wing UAV for provisioning mobile video streaming services to multiple GUs. We define UAV's energy efficiency as the ratio of perceived video quality at all GUs to the UAV's energy consumption and formulate an energy efficiency maximization problem that jointly optimizes the communication time allocation among GUs and the UAV's trajectory. Due to the non-convex nature of the formulated problem, we propose a near-optimal iterative algorithm, which utilizes successive convex approximation and quadratic transform techniques to address the problem efficiently. Extensive simulations demonstrate the high efficiency and effectiveness of our proposed algorithm.

With the rapid growth of Internet of Things and proliferation of handheld smart devices, mobile crowdsensing has been regarded as an effective sensing paradigm due to its high scalability, low cost, and wide coverage. In this paper, we study hybrid task assignment where semi-opportunistic and participatory users co-exist for task executions while tasks are delay sensitive and have heterogeneous qualities. The design objective is to maximize the total quality of completed tasks subject to a total budget shared by both types of users. We formulate this problem as an integer programming problem. We propose an efficient hybrid users based task assignment algorithm (referred to as HU-TSA), which works in an iterative way as follows. It first selects the top n (initially, n = 1) semi-opportunistic users in terms of quality-cost ratio for task assignment. It then clusters the remaining tasks into different regions based on their closeness and then performs utility based optimized user-region binding and standardized task density based path planning for the participatory users. It repeats the above process over all possible values of n to seek an optimal budget splitting between the two types of users for improved performance. We present the detailed design description of HU-TSA and deduce its computational complexity. Extensive simulations are carried out and the results show the effectiveness of HU-TSA by comparing with existing algorithms.

Scalable consensus protocol is essential for providing high-throughput and secure blockchain services in wireless networks. In this article, we propose a scalable credible-committee-based blockchain consensus (SCBC) protocol for resource-limited multihop wireless networks, which contains the following key designs: 1) credit-based committee selection algorithm, which improves the system security by selecting credible committee members; 2) scalable credible-committee-based consensus algorithm, which supports efficient consensuses using small-sized committee and threshold signatures; and 3) criticality-based localized broadcast algorithm, which is designed to suppress broadcast redundancy and further improves the consensus efficiency. Thorough security analyses show that SCBC satisfies both safety and liveness properties, and can resist more attacks than traditional Byzantine fault-tolerant consensus protocols. We deduce the message complexity of SCBC. Extensive simulation results demonstrate that our proposed protocol SCBC outperforms existing work in terms of throughput and consensus latency.

Fingerprint-based indoor localization is one of the most promising solutions for various Intelligent Internet of Things (IIoT) systems. However, recent studies show that the key design challenges of current fingerprint-based localization techniques come from the following three aspects: 1) temporal variation caused by various patterns of IIoT device operations and stochastic fluctuation of wireless signals, 2) spatial unevenness of collected RSSI samples due to complex multi-floor environments, and 3) high feature sparsity of collected RSSI samples in large areas. To address these challenges, we present a localization architecture for multi-floor indoor localization in multi-building environment and accordingly propose a fingerprint-based localization method (referred to as GrowNetLoc) based on Gradient Boosting Neural Network (GrowNet) and Long Short-Term Memory (LSTM) network. Regarding building/floor identification, the gradient ensemble model GrowNet is utilized for extracting the mapping relationship between uneven RSSI samples and building/floor indices. Regarding location estimation, LSTM network is adopted as one layer of base learner to extract temporal features of RSSI samples, and a gradient boosting strategy is further used for overcoming the sample sparsity issue and improving the location estimation performance. Extensive experiments are conducted on real datasets and the results demonstrate that GrowNetLoc has superior localization accuracy and robustness performance compared with the existing methods.

Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.

Disseminating continuous packet flows injected at multiple location-random source nodes to all network nodes, known as the multi-source dynamic global broadcast problem, is a fundamental building block for wireless multi-hop networks to run smoothly and efficiently. Previous studies on dynamic global broadcast all assume reliable communications. However, in realistic wireless networks, there exist unpredictable transmission failures caused by the randomized signal interference from uncorrelated wireless networks sharing the same spectrum or even malicious attackers. In this paper, by integrating the Signal-to-Interference-plus-Noise-Ratio (SINR) model, multi-channel communication mode, and randomized malicious channel jamming controlled by an adaptive adversary, we present an SINR-based adversarial channel jamming model to capture the unpredictable transmission failures in a wireless multi-hop network. We first propose a distributed Jamming-resilient Multi-source Static Broadcast (JMSB) algorithm based on random channel selection and message transmissions for multi-hop wireless networks under the above SINR-based adversarial channel jamming model. We then propose a distributed stable Jamming-resilient Multi-source Dynamic Broadcast (JMDB) algorithm which iterates JMSB repeatedly and efficiently in a two-stage manner. We derive the maximum supportable broadcast throughput of JMDB under the stability guarantee, i.e., the expected boundedness on the queue length of each network node and expected broadcast latency for each injected packet. Simulation results shows the stability and throughput efficiency of our proposed JMDB algorithm.

Consensus protocol is a key technology enabling blockchain to provide secure and trustful services in wireless networks. However, most previous study on blockchain consensus protocols for wireless networks relies on reliable message transmissions and honest leaders. In practice, wireless blockchains inherently suffer from limited physical resources and unreliable wireless channels due to environmental noises and adversary attacks. This paper studies the design of Byzantine fault tolerant consensus protocol for blockchain in single-hop wireless networks subject to signal-to-noise constraint. For this purpose, we propose a low-latency and reliable Byzantine fault-tolerant consensus protocol LRBP, which incorporates the following three designs: 1) Randomized credit-based block proposer selection, which can prevent adversary corruption and improve the system throughput, 2) Enhanced threshold Boneh-Lynn-Shacham signature based voting mechanism, which can achieve communication-efficient block validity voting by using piggyback-based acknowledgment and criticality-based adaptive channel accessing probability adjustment, and 3) Random linear network coding based batch forwarding, which supports reliable block transmissions. We derive the consensus success probability and consensus time complexity of LRBP. We prove that LRBP simultaneously satisfies the properties of persistence and liveness. It is resistant to the 51% attack, Sybil attack, double-spending attack, and jamming attack. Simulation results show the high efficiency of LRBP as compared with existing work.

Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.

Computing power network (CPN), which integrates heterogeneous computing resources and communication network, can tackle the challenges brought by the pervasiveness of mobile and Internet of Things applications. In this paper, we study the optimization of task scheduling in a CPN network by considering the unbalancing between task distribution and resource cost. The design objective is to maximize the system profit while satisfying tasks' delay requirements. We formulate this problem as an integer programming problem. To address this NP-hard problem, we propose a Deep Reinforcement Learning (DRL) based multistage profit-aware task scheduling algorithm which first makes coarse grained task allocation using DRL among regions and then determines an optimized intra-region task assignment by using profit-aware balancing algorithm. Extensive simulations are conducted for performance evaluation and the results show the high performance of the proposed algorithm as compared with baseline algorithms.

Mobile crowdsensing (MCS) has been an effective sensing paradigm by utilizing the smart devices carried by mobile users to complete sensing tasks at different locations. An important problem in MCS is how to achieve effective task assignment in the context of opportunistic sensing, where mobile users are selectively recruited to perform tasks in an opportunistic way. However, most existing work in this aspect suppose there are only one service platform and further the sensing qualities of users are known a priori. In this article, we study the task assignment when there are multiple service platforms and further the sensing qualities of users are unknown a priori. The design objective is to maximize the overall sensing qualities of finished tasks at all platforms. For this purpose, we build a multiplatform cooperation framework and formulate the task quality maximization problem in this case as a 0–1 integer linear programming (ILP) problem. We propose a multiplatform-cooperation-based task assignment mechanism (MCTA). MCTA includes two phases. The first phase establishes stable cooperation relationship among platforms while respecting their respective cooperation willingness, and for this phase, we propose a cross-platform cooperation relationship construction algorithm. The second phase performs effective online task assignment, and for this phase, we propose two online multiarmed bandit (MAB) with sleeping -arms-based user selection algorithms using local and global learning, respectively, based on whether cross-platform user-sensing-quality learning is allowed. We derive the regrets of the proposed algorithms and prove that MCTA has the properties of cooperation stability and computation efficiency. Extensive simulation results show the high performance of our proposed MCTA mechanism as compared with the existing work.

Multi-source global broadcast is a fundamental problem in multi-hop wireless networks. The Static Multi-source Global Broadcast problem (SMGB), which considers static packet injection at all source nodes, has been extensively studied in recent years. However, packets are more likely to be continuously injected over time in realistic multi-hop wireless networks. In this paper, we focus on studying the Dynamic Multi-source Global Broadcast problem (DMGB), in which packets are continuously injected to$k$($k\geq 2$) source nodes in the network according to a widely-used dynamic packet injection model and the objective is to disseminate each injected packet across the whole network quickly. We solve this DMGB problem under the Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. Specifically, we first present a distributed randomized algorithm for solving the SMGB problem. We then iterate this SMGB algorithm repeatedly to construct a distributed DMGB algorithm. We prove the proposed DMGB algorithm is stable, i.e., the expected number of packets in each node’s message queue is bounded at any time and further the expected global broadcast latency for each injected packet is bounded. Simulation results validate the effectiveness of the proposed DMGB algorithm.

Multi-access edge computing (MEC) has been an important and promising paradigm for offering computing services to mobile users with computation-intensive and latency-critical tasks. In this paper, we study a D2D collaboration based MEC system, where the service platform purchases resources from resource-rich collaborative D2D devices when the task arrival rate exceeds the platform’s capability for providing satisfactory QoS. The design objective is to maximize the platform profit while maximally satisfying the delay requirements of tasks. We define delay based utility functions for different participants and accordingly formulate the platform profit maximization problem as a Mixed Integer Non-Linear Programming (MINLP) problem. For the online case where future task arrivals are unknown in advance, we propose a reverse auction based task assignment and urgency-value based transmission scheduling algorithm (RAGM). We present the detailed algorithm design and deduce its computation complexity. We prove that RAGM satisfies individual rationality of all participants. We conduct extensive simulations and the results show the high performance of RAGM as compared with benchmark algorithms.

Opportunistic Mobile Crowdsensing (MCS) is an attractive and cost-effective sensing paradigm because it does not affect workers' daily routines. However, its opportunistic nature in task executions can lead to low task completion rate for deadline-sensitive tasks as compared with participatory sensing. Besides, existing work in opportunistic M CS lacks of study on how to effectively coordinate among multiple service platforms for idle worker sharing so as to improve the sensing performance. In this paper, we design a multi-platform cooperation based incentive mechanism (MPCIM) for deadline-sensitive task assignment in the context of opportunistic mobile crowdsensing. The design objective is to maximize the system profit while improving the task completion rate. In MPCIM, each platform first decides how many idle workers it can provide and also how many tasks it needs assistance at different locations; Then, a cross-platform managing entity is responsible for performing maximal matching between the idle workers and excessive tasks among different platforms to improve the task completion rate while respecting individual rationality and cooperative rationality. Extensive simulation results show that MPCIM can effectively improve the system profit and also task completion rate.

Mobile crowdsensing has become a promising sensing paradigm with the popularization of mobile devices. In this paper, we focus on an opportunistic mobile crowdsensing scenario where there are multiple task requesters and users, who move in an opportunistic way in the target environment. When a task requester encounters a user, he can assign some of his held tasks to the user and receive corresponding task results when they re-encounter sometime later. In this paper, we study how to minimize the largest makespan of all requesters for the task result collections. To address this issue, we propose a cluster based largest makespan sensitive online task assignment (C-LOTA) algorithm. C-LOTA first performs two-phase clustering which clusters the users into different clusters, one for each task requester, based on their relativeness to the task requesters and also the task workloads at different requesters. C-LOTA then iteratively performs greedy intra-cluster task assignment such that largest task is firstly assigned and the first idle user always takes the task, until all tasks are assigned. We present the detailed algorithm design of C-LOTA. We deduce its computation complexity. Simulation results show that C-LOTA can achieve much better performance compared with existing work.

The explosive development of short video applications has brought severe pressure on radio resources at hotspot areas. The features of short video recommendations-and-pushing techniques provide us an opportunity to relieve the radio resource pressure via wireless multicast: An edge server can be deployed at the base station, which receives short video lists recommended by remote video server and then pushes such mobile video services to local users through wireless multicast. In this paper, we study how to reshuffle the video lists received from remote server so as to facilitate wireless multicast to maximally reduce the required wireless resource while considering the fact that a user client can only buffer one short video for watching based on off-the-shelf short video APPs. We formulate the problem of video list reshuffling for minimizing the total wireless resources consumption as an integer programming problem. We design a Minimum degree of Freedom based Maximum Filling video reshuffling algorithm (MFMF) to address this problem. MFMF moves videos from the original video lists into same sized but reshuffled video lists in a greedy manner, once for a video, whose moving can satisfy the most reshuffled video lists, and if multiple such choices exist, selects the one having the least position options. This process continues until all the videos are moved. We deduce the computation complexity of MFMF. Numerical results demonstrate the significantly high performance of MFMF.

How to effectively organize various heterogeneous clients for effective model training has been a critical issue in federated learning. Existing algorithms in this aspect are all for single model training and are not suitable for parallel multi-model training due to the inefficient utilization of resources at the powerful clients. In this paper, we study the issue of multi-model training in federated learning. The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training and therefore maximize the overall training efficiency while ensuring a certain fairness among individual models. For this purpose, we introduce a logarithmic function to characterize the relationship between the model training accuracy and the number of clients involved in the training based on measurement results. We accordingly formulate the multi-model training as an optimization problem to find an assignment to maximize the overall training efficiency while ensuring a log fairness among individual models. We design a Logarithmic Fairness based Multi-model Balancing algorithm (LFMB), which iteratively replaces the already assigned models with a not-assigned model at each client for improving the training efficiency, until no such improvement can be found. Numerical results demonstrate the significantly high performance of LFMB in terms of overall training efficiency and fairness.

Drones (also known as unmanned aerial vehicles) has been widely utilized to enhance the performance of mobile crowdsensing (MCS) where the drones can greatly increase the service range by serving as mobile task executors. In this paper, we consider the joint use of truck and drones for effective task execution in the MCS model. In this model, drones are dispatched to depart from the truck, execute one or multiple tasks, and converge with the truck for data collection and battery replacement. In this process, the truck can serve as a mobile drone hub as well as a mobile task executor. The design objective is to minimize the overall cost for truck movement, drone flying, and driver payment in the whole MCS process. We formulate this problem as a mixed integer linear programming (MILP) problem. Due to the NP-hardness of this problem, we propose an efficient algorithm based on variable neighborhood search for efficient joint task assignment and path planning. We conduct numerical experiments and the result demonstrate the effectiveness of the proposed algorithm and the advantage of joint use of truck and drones for mobile crowdsensing.

Mobile crowdsensing is a new paradigm for data collection by utilizing the mobility of sensor-rich hand-held smart devices. One of the key challenges in mobile crowdsensing is how to effectively assign tasks to mobile users in an online manner. In this paper, we study the online task assignment problem in mobile crowdsensing where each task has specific time window for its sensor data collection. The objective is to maximize the total profit of the platform in whole sensing period. We first model the crowdsensing system and formulate the profit maximization problem under study. To address this problem, we propose two heuristic algorithms, one is bipartite-match-based algorithm (BMA) using Kuhn-Munkres algorithm and the other improves the first by using data offloading for data upload cost reduction, if applicable. We present detailed algorithm design for both algorithms and deduce their computational complexities. Finally, simulation results validate the effectiveness of our proposed algorithms.

In this paper, we study the design of opportunistic routing for efficient data offloading in vehicular opportunistic networks with the assistance of sparsely deployed static relays. The objective is to maximize the data offloading ratio and also reduce the average delivery delay while respecting the data’s delay requirement. For this purpose, we propose a sparse relay assisted opportunistic routing algorithm for efficient data offloading. We show how to efficiently utilize the encounters with static relays for improved data offloading performance based on vehicles’ trajectories. We further design a greedy algorithm for optimized relay deployment. We reduce the computational complexity of the data offloading algorithm and also the relay deployment algorithm, respectively. Simulation results demonstrate the high performance of our proposed algorithms.

Traffic light control is vital for the efficiency of urban transportation. Recently, the increasing of vehicles has brought great challenges to the traffic light control system. However, traditional traffic light controlling methods are inefficient due to the sophistications of traffic dynamics. In this paper, we propose a Graph Attention mechanism based Multi-Agent Reinforcement Learning method (GA-MARL) by extending the Actor-Critic framework to improve the efficiency of cooperation in traffic signal control. The proposed algorithm is based on hard-attention and soft-attention mechanism, which can help agent filter information effectively and calculate the importance of other agents. In addition, we complete our algorithm by adopting the framework of Centralized Training with Decentralized Execution (CTDE) to overcome the challenge of non-stationary non-Markovian environments. Simulation results prove that our proposed method outperforms the representative methods in the literature.

Intelligent reflecting surfaces (IRSs) have been proposed in recent years as a promising technology to enhance the quality of transmissions in high-frequency spectrum. Currently, the research on the performance of large networks with IRSs is still in its infancy. Different from the commonly-used stochastic geometry model for the study of traditional networks, where only transmitters and receivers are modeled as point processes, in an IRS network, the blockages and reflectors also need to be accounted for. In this paper, we study a bipolar network with a line segment object model, and derive the probability that an IRS can successfully reflect a signal from a transmitter to a receiver, as well as the distribution of the distance traveled by the reflected signal. With these analytic results, the signal to interference ratio (SIR) and the achievable rate are obtained in closed-form expressions. From the analysis, we can observe that IRSs have a great potential to enhance the network performance, as they are able to boost the signal power, while preventing the inter-cell interference from rising rapidly. More importantly, we find that even with a limited number of IRSs, the network can still achieve a higher achievable rate than a conventional one without IRSs.

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

With the rapid development of mobile smart devices, mobile crowdsensing has become an attractive paradigm for sensor data collection. In a mobile crowdsensing system, the platform can publish a set of tasks and then recruit suitable mobile users to accomplish these tasks. In this paper, we study the budget-constrained task assignment problem for mobile crowdsensing. We assume users can choose to take different transportations for task execution, and different choices have different task coverages, travel expenses, and travel time. We model the crowdsensing system and formulate the budget-constrained task assignment problem under study. We prove this problem is NP-hard. To address this problem, we propose a Value/Reward Maximum First heuristic algorithm (VRMF). We present the detailed algorithm design and deduce its computational complexity. Simulation results validate the effectiveness of our proposed algorithm.

Batch forwarding using intra-flow random linear network coding (RLNC) has been used to improve the performance of a wireless network constituent of lossy links. However, existing batch-based forwarding mechanisms in this aspect can lead to a lot of bandwidth waste and thus reduced transmission efficiency. In this paper, we design a Sliding WIndow based Multiple batch forwarding mechanism (SWIM) using RLNC. In SWIM, multiple batches are allowed to be sent out simultaneously in a way that the forwarding process is managed by a sliding window. In SWIM, adaptive rate assignment is used to assign bandwidth resources to different batches based on their decoding states at the destination, in order to make full use of the bandwidth resources. Simulation results show that SWIM can achieve improved throughput performance as compared with existing work.

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.

Mobile crowdsensing is a novel paradigm by exploiting mobility, sensing, computation, and communication capability of smart devices. In this paper, we study data offloading problem for mobile crowdsensing in opportunistic social networks. In this scenario, mobile users can upload sensing data directly via cellular networks using various data plans. A mobile user can also resort to another user for data offloading by forwarding sensing data to that user using short-range communications (when they encounter). To minimize total data uploading cost while meeting given uploading deadlines, data plan assignment for users and data forwarding strategy when two users encounter should be elaborately designed. In this paper, we use Benders decomposition algorithm to solve offline data plan assignment problem. Then we propose two algorithms including progress- balanced algorithm and social-aware forwarding algorithm to solve online data forwarding problem. Simulation results show that data offloading between users can largely reduce the total data uploading cost. Simulation results also show that the performance of our proposed online algorithms is close to the offline optimal solution.

With the widespread of smart devices, mobile crowdsensing has become an attractive way to perceive and collect sensing data. In this paper, we focus on studying AP-assisted task assignment in mobile crowdsensing. The objective is to effectively reduce the average or worst-case makespan of tasks. We focus on a scenario that a task requester needs the assistance of mobile users for task accomplishment while they can meet directly or via APs in an opportunistic manner. We model the crowdsensing system and then formulate the problems under study. We then propose an AP-assisted average makespan sensitive online task assignment (AP-AOTA) algorithm and an AP-assisted largest makespan sensitive online task assignment (AP-LOTA) algorithm. In the proposed algorithms, task assignment at each step considers both the inter-encountering time between requester and each user and that between them while going through APs. We present design details of the proposed algorithms. We derive their computational complexities to be O(mn2), where m is the number of tasks and n is the number of users. Finally, trace-driven simulation results show that the proposed algorithms outperform existing work.

Mobile crowdsensing is a new sensing paradigm exploiting potential of crowds to collect data, which has various advantages over traditional sensor networks such as low cost, high coverage, and high mobility. Privacy preservation is a crucial issue in mobile crowdsensing because worker privacy might be exposed if workers share their location information to service platform or other workers. In this paper, we assume workers can determine their own privacy preservation levels and they do not need to upload their location information to the platform or share to other workers for sensing behavior coordination. Moreover, workers move to task locations to collect sensing data in a distributed manner. We accordingly propose a privacy-aware online task assignment framework to achieve high task coverage. In this framework, spatial task-application information in previous cycles is used to estimate worker density and an incentive pricing mechanism is designed to guide workers to collect sensing data in low-worker-density areas. We present detailed mechanism design. Extensive simulation results show that our proposed solution has much better performance than the baseline mechanism.

Incentive mechanism design is a critical issue in mobile crowdsensing and a lot of work has been carried out. However, existing mechanisms in this area generally lack of consideration of individual worker/candidate's (potential) contribution to the system when recruiting new workers or when detaining existing workers. In this paper, we design a reverse auction based incentive mechanism. The design objective is to maximally reduce the system maintenance cost (including auction cost and recruitment cost) by optimizing the composition of workers in the system. For this purpose, in the recruiting process, candidates are queried in the descending order of their potential contributions to the system, while in the detaining process, likelydropping-out workers are rewarded with inner lottery whose amount is adjusted based on their usefulness to the system. In the auction process, prices are calculated based on workers' bids and also their usefulness to the system. We present detailed mechanism design. Simulation results show that our mechanism outperforms existing work.

This paper studies the downlink spectrum resource allocation problem in a small cell network and focuses on mitigating intra-tier interference between different small cells. A two-tier clustering based downlink resource allocation (TCRA) algorithm is proposed to perform spectrum resource allocation. To increase spectrum utilization, the algorithm allows different small-cell user equipments (SUEs) to share the same physical resource blocks (PRBs). Meanwhile, to mitigate intra-tier interference, those SUEs who are close to each other in distance are avoided to share the same PRBs as much as possible. To implement this, a two-tier clustering approach is introduced in PRB allocation. In the first tier, all small cells in the system are partitioned into a set of small cell clusters based on graph coloring, and those small cells with the same color are partitioned into the same cluster. In the second tier, all SUEs in each small cell cluster are further partitioned into a set of user clusters based on the interference graph of each small cell cluster. After the two-tier clustering, PRB allocation is performed based on the user clusters obtained. Simulation results show that the proposed TCRA algorithm can significantly improve the system performance in terms of total system capacity.

Efficient resource allocation which aims to maximize the network utility under energy neural operation is well known as a key issue in energy harvesting wireless sensor networks (EHWSNs). However, as the energy resource is unstable in practical systems, it's challenging to tackle the uncertainty in harvested energy profile. Instead of designing sophisticated harvested energy prediction model, we directly make uncertainty involved in the resource allocation design. Considering the uncertainty of harvested energy profile, a flexible network utility optimization approach is proposed that can achieve high network utility and robustness against uncertain harvested energy. We firstly formulate the network utility maximization problem subject to energy constraints involving uncertainty. We then introduce a flexible uncertainty model to describe the harvested energy and transform the network utility maximization with uncertainties into a traditional optimization problem. Our experimental results demonstrate the proposed approach is able to provide flexible energy allocation and achieve robustness.

Free-viewpoint television enables users to view a scenario from arbitrary viewpoint as if they are physically in the scenario and can watch the scene freely. Thus, Free-viewpoint television can provide immersive experience of physical event broadcast, especially for large-scale live vocal concert broadcasts. However, huge bandwidth demand is a major challenge faced by free-viewpoint television broadcast since many streaming with different view angles are needed for users' selection. In this paper, we propose a scheme named P2P transcoder to realize free-viewpoint video streaming transmissions. It selects a subset of users to work as transcoders and these transcoders will produce video rates/angles that other remaining users request. Thus the total amount of traffic to deliver is greatly reduced and more saved bandwidth can be used to improve the video quality. We further build a model for achieving optimal bandwidth allocation using this scheme. Numerical results show that the proposed scheme can significantly improve the video quality as compared with existing work.

Mobile crowdsensing is a sensing paradigm such that mobile users need to move to task locations to perform sensing tasks. In this paper, we focus on studying the task assignment problem of eco-friendly mobile crowdsensing which aims to minimize carbon emissions while meeting various resource limits including task deadlines and transportation constraints. We first describe the eco-friendly mobile crowdsensing system model and formulate the task assignment problem. Then we divide the problem into two subproblems including selection of best transportation type and user-task matching. We model the user-task matching as unbalanced minimum-cost bipartite matching, transform the problem into balanced maximum-weight bipartite matching, and use Kuhn-Munkres algorithm to obtain the optimal solution. Extensive simulations are conducted and the results show the efficiency and effectiveness of our proposed solution.

Compressed sensing for wireless sensor networks has attracted a lot of research attention in the last decade for its advantages in energy saving, robustness, and so on. Nevertheless, existing solutions mostly focus on the data compression performance while neglecting the energy efficiency. In this paper, we first present the joint resource allocation problem formulation based on compressed sensing. Then a distributed algorithm to compute the sampling rate and routes utilizing local network status is proposed. We conduct extensive experiments based on meteorological wireless sensor networks to verify the merit of our mechanism; it is shown that the proposed mechanism is able to achieve very high efficiency in terms of network lifetime and sensing quality compared with existing approaches.

The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high‐quality streaming Video‐on‐Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low‐quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay.

Smart devices with a rich set of low-cost sensors enable a new sensing paradigm called mobile crowdsensing. In mobile crowdsensing, tasks are distributed at a variety of locations. Mobile users travel through different task locations to perform different tasks. The diversity of task locations and user trajectories makes the optimal scheduling problem intractable. In this paper, we mathematically formulate the optimal task scheduling problem as a continuous path planning problem, which is known to be NP-hard. Then we propose two online heuristic algorithms to maximize the task quality improvement for each newly arriving user. These algorithms work in a hop by hop manner for task selection and adopt different measures and strategies including: (1) ratio of task quality increment and travel cost and (2) task spatial density. We present detailed algorithm design and deduce their computational complexity. Extensive simulation results show that our algorithms outperform existing work.

Opportunistic routing has been a promising routing paradigm for the performance of a wireless sensor network (WSN) due to the broadcast and lossy characteristics of wireless channels. In this paper, we propose a Correlation Aware opportunistic Routing protocol CAR, which combines spatial correlation based data aggregation and opportunistic routing for achieving improved routing performance. The design behind CAR is to fully take advantage of the characteristic of spatial correlation among data sensed by neighbor sensor nodes and opportunistic nature for data reception in packet delivery in wireless channels. For this purpose, CAR first aggregate correlated data at selected forwarder and then opportunistically forward aggregated data toward the intended destination to reduce data redundancy. Extensive simulations show that CAR outperforms existing work in terms of data delivery cost and overall energy consumption.

Aggregating multiple access interfaces of a client is a promising way to satisfy the high bandwidth demand by high-definition video streaming services. However, how to efficiently use such aggregated bandwidth to improve the reliability of timely fetching video contents needs to be further studied. In this paper, we propose a partial overlapping chunk based dual-path transmission scheme, which uses partial redundancy based transmissions to optimize the playback performance at the client side. Specifically, we schedule the transmissions of different sized chunks with partial overlapping according to the delivery capabilities of different paths. We then build an optimal model to compute the optimal overlapping ratio between the transmitted chunks to maximize the probability of timely fetching of video contents. Numerical results demonstrate that, our scheme can improve the probability of timely fetching video contents, by up to 19.3%, compared to the traditional scheme without chunk overlapping, while the incurred average transmission redundancy is below 14.4%.

Minimizing the maximum sensor movement distance for achieving linear barrier coverage is a key issue for intrusion detection in the area of barrier coverage. Existing work in this area mostly assume that every working sensor needs to move onto the barrier line. Actually, sensors can still be helpful for covering the barrier when they move near enough to the line. In this paper, we study the case where moving sensors do not have to move onto the barrier line for intrusion detection purpose, and accordingly present an optimal algorithm for this issue. Detailed algorithm design is presented and the complexity of the algorithm is deduced to be O(n2log2|M|), where n represents the number of sensors in the network and M represents the upper bound of possible moving distance. Simulation results show that our algorithm outperforms existing work.

Data collection is key issue in vehicular networks since it is vital for supporting many applications in vehicular environments. With the explosive growth of sensing data in urban area, however, strategies for efficient collection of big data in vehicular networks are still far from being well studied. In this paper, we focus on studying this issue and accordingly propose a Software Defined Vehicular Networks (SDVN) architecture. On this architecture, a predictive data collection algorithm is proposed. In this algorithm, packet delivery is fulfilled by cooperative cellular and ad hoc network interfaces, in which collections of big data always adopts ad hoc based multi-hop relaying whenever applicable to forward packets to Road Side Units (RSUs). Cellular networks are used for data uploading only when no multi-hop relaying opportunity is available. Our proposed SDVN architecture enables such efficient cooperative communications, in which predictive routing decisions are made based on real-time network status other than empirical knowledge. Simulation results demonstrate that our algorithm outperforms existing algorithms in terms of packet delivery ratio and transmit efficiency.

Mobile data offloading benefits both end users and content providers for enhancing user experiences and more data cost effectiveness, thus attracted lots of researchers' efforts on studying new offloading opportunities and optimized solutions. However, it is still under-explored in subway environment and this comes more valuable as more users are taking subway as daily means of transport. Indeed, motivated by special data offloading opportunities found in a subway train environment for users, we designed a local data distribution model and a super node selection algorithm based on context information and node resources, by combining the characteristics of users' interests on various contents, users' behavior and resources availability. Simulation results clearly show the high efficiency of our data distribution model and super node selection algorithm for offloading cellular data by as high as 90%.

Real-time video communications has been incorporated into many instant communication tools such as ichat, Skype, QQ, etc. Real-time video communications has low delivery delay requirement, which imposes great challenge to the provisioning of such services. To address this problem, in this paper, we propose a selective redundant transmission mechanism to support real-time streaming on multi-interface wireless terminals. This mechanism selectively duplicates some video frames according to the tightness of their lifetimes and further schedule their transmissions (or some of them) via neighbors' assistance. We build a model to select the optimal encoding rate and also the optimal per-frame copy number in order to maximize the peak signal noise ratio (PSNR) of video streaming service when maximal allowable total traffic rate is given. Numerical results show that the proposed mechanism can significantly improve the PSNR of real-time video streaming as compared with existing work.

Recently, research on wireless sensor networks with mobile sinks (mWSN) has attracted a lot of attention. The mobility of such sinks often results in unpredictable changes of network topology, and brings big challenge to the design of efficient routing protocols for such networks. In this paper, we focus on design of an energy efficient distributed routing protocol for mWSNs. For this purpose, we propose a lightweight ring-based bidirectional routing protocol, referred to as BI-LRRP. BI-LRRP does not need location information and it performs ring-based routing on multi-ring based network structure for packet delivery. To reduce the transmission cost and also prolong the network lifetime, BI-LRRP uses bidirectional search for finding a mobile sink before actual packet delivery. Simulations results show that the proposed protocol can achieve high performance as compared with existing work.

The emerging wireless energy transfer technology based on Radio Frequency (RF) is a promising technology for wireless rechargeable sensor networks (WRSN) as it can charge sensor nodes simultaneously. In this paper, we use a mobile charger to stay at some locations and stay for certain time at each location to charge all the nodes in the network. We first define a power-charging function for the whole network and then get a set of candidate stop locations for the mobile charger by analyzing the property of this function. After the set of candidate locations are determined, we formulate two optimization problems: one is to minimize total charging time and another is to maximize the charging efficiency, subject to a charged energy threshold at each sensor node. Simulation results show that our method for choosing stop locations can greatly reduce the total charging time and improve charging efficiency.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a while since its last discharging. Accordingly, the battery can work for a longer time when it takes some rest between consecutive discharging processes than when it works all the time. However, to the best of our knowledge, this impact has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm for wireless sensor networks. In this algorithm, each node in the network periodically decides to be in the dominating set or not. Nodes that have taken sleep in the preceding round are encouraged to involve in the dominating set in the current round while nodes that have worked in the preceding round are encouraged to sleep in the current round for battery recovery. Detailed design description is presented. The complexity of the proposed algorithm is deduced to be O(D2), where D represents node degree. Simulation results show that our algorithm can significantly improve the network lifetime performance as compared with existing work.

With the increasing popularity of WLAN infrastructure, WiFi fingerprint-based indoor positioning systems have received considerable attention recently. Much existing work in this aspect adopts classification techniques that match a vector of radio signal strengths (RSSs) reported by a mobile station (MS) to pretrained reference fingerprints sampled from different access points (APs) at different reference points (RPs) with known positions. However, in the calculation of signal distances between different RSS vectors, existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal differences in geometrical distances in complex indoor environment. To address this issue, in this paper, we propose a feature-scaling-based k-nearest neighbor (FS-kNN) algorithm for achieving improved localization accuracy. In FS-kNN, we build a novel RSS-level-based FS model, which introduces RSS-level-based scaling weights in the computation of effective signal distances between signal vector reported by a MS and reference fingerprints in a radio map. Experimental results show that FS-kNN can achieve an average location error as low as 1.70 m, which is superior to existing work.

ABSTRACT Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location‐based topology control algorithms have been proposed. In this paper, we propose two location‐assisted grid‐based topology control (GBP) algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid‐based topology control, a network is divided into equally spaced squares (called grids). We accordingly design cross‐sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near‐minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross‐sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE-CDS) for wireless sensor networks. In BRE-CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE-CDS is deduced to be O(D2), where D is node degree. Simulation results show that BRE-CDS can significantly prolong the network lifetime as compared with existing work. Copyright © 2016 John Wiley & Sons, Ltd.

Much previous work had demonstrated the remarkable performance of backpressure based routing and scheduling algorithms in wireless sensor networks (WSNs). However, the absence of consideration on energy use efficiency in the design of existing backpressure based algorithms makes them difficult to be deployed in resource-limited WSNs. In this paper, we study how to improve the energy use efficiency of backpressure based algorithm. For this purpose, we propose an energy efficient backpressure routing and scheduling algorithm (EBP) for WSNs. In EBP, a new link weight calculation method is designed, based on which nodal energy status is considered when making decisions on backpressure based transmission scheduling. In EBP, packets are encouraged to be forwarded to nodes with more residual energy while the throughput-optimality of backpressure based algorithm is still preserved. Simulation results show that EBP can obtain significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work.

In this paper, we study how to deploy new access points (AP) to achieve improved accuracy for WiFi- based indoor localization systems. Existing mechanisms in this aspect are typically simulation based and further they do not consider how to use pre-existing APs in target environment for achieving high localization performance. To overcome these issues, in this paper, we propose a measurement-based AP deployment mechanism (MAPD). MAPD takes advantage of those pre-existing APs to identify candidate positions with poor localization accuracy for deploying new APs. We then collect the fingerprints for all possible AP deployment layouts via over- deployment of APs, one at each candidate position. Finally, we present a greedy search algorithm to identify m positions out of the n candidate positions (mn) while minimizing the location error. Experimental results demonstrate that the localization errors can be largely reduced: Mean error distance can be reduced by 0.56 meter (26%) and 0.17 meter (10%) as compared with the case without deploying new APs and previous work, respectively; Moreover, the maximum location error can be reduced by 1.53 meter (27%) and 0.51 meter (11%), respectively.

Recently, inter-session opportunistic network coding has been considered as a promising technology for improving the performance of a wireless multi-hop network (WMN). However, most existing work in this field did not consider the issue of how the wireless medium is accessed could largely affect the performance of localized network coding. In this paper, we theoretically analyze the throughput improvement obtained by combining network coding and transmission scheduling in a WMN. Then we formulate the optimal throughput problem as a minimum length scheduling problem subject to potential coding opportunities and coding based transmission conflict constraints. We further propose a distributed coding aware transmission scheduling mechanism for WMNs. Simulation results show that our proposed mechanism can remarkably improve the network throughput as compared with existing work.

Recently, the development in full-duplex communications has offered a great opportunity to perform simultaneous spectrum sensing and spectrum access in cognitive radio networks. In this paper, we consider a cognitive cellular network, in which the secondary base station (SBS) is a full-duplex device that can simultaneously sense the primary spectrum and transmit to the secondary users. We show that the power allocation of the SBS can affect both the sensing performance and the transmission capacity, and thus, we jointly consider the power allocation problem in the spectrum management process. First, we formulate the considered problem as a 3-dimensional matching problem and prove its NP-hardness. Then, we propose an approximate solution by extending a 2-dimensional matching algorithm. The simulation results show that the proposed algorithm can highly increase the secondary throughput of the SBS, compared with the greedy algorithm and the random algorithm.

In this paper, we study how to effectively reduce the average end-to-end (E2E) packet delay in backpressure based scheduling in wireless multi-hop networks. We accordingly propose a virtual gradient based back-pressure scheduling algorithm, referred to as VBR. In VBR, intentional virtual queue, whose length (called virtual gradient) depends on the distance to destination, is first built at nodes in a network in the network configuration phase. In this way, virtual gradient is established at nodes in the network. In the network operation phase, the scheduling decision at each node needs to jointly consider both real queue length and virtual queue length. Simulation results show that VBR can obtain significant performance improvement on back-pressure based routing and scheduling, in terms of packet delivery ratio and average E2E delay.

Deadline guaranteed packet scheduling for switches is a fundamental issue for providing guaranteed QoS in digital networks. It is a historically difficult NP-hard problem if three or more deadlines are involved. All existing algorithms have too low throughput to be used in practice. A key reason is they use packet deadlines as default priorities to decide which packets to drop whenever conflicts occur. Although such a priority structure can ease the scheduling by focusing on one deadline at a time, it hurts the throughput greatly. Since deadlines do not necessarily represent the actual importance of packets, we can greatly improve the throughput if deadline induced priority is not enforced. This paper first presents an algorithm that guarantees the maximum throughput for the case where only two different deadlines are allowed. Then, an algorithm called iterative scheduling with no priority (ISNOP) is proposed forthe general case where k > 2 different deadlines may occur. Not only does this algorithm have dramatically better average performance than all existing algorithms, but also guarantees approximation ratio of 2. ISNOP would provide a good practical solution for the historically difficult packet scheduling problem.

Abstract In this paper, we study the issue of routing in a vehicular ad hoc network with the assistance of sparsely deployed auxiliary relay nodes at some road intersections in a city. In such a network, vehicles keep moving, and relay nodes are static. The purpose of introducing auxiliary relay nodes is to reduce the end‐to‐end packet delivery delay. We propose a sparsely deployed relay node assisted routing (SRR) algorithm, which differs from existing routing protocols on how routing decisions are made at road intersections where static relay nodes are available such that relay nodes can temporarily buffer a data packet if the packet is expected to meet a vehicle leading to a better route with high probability in certain time than the current vehicles. We further calculate the joint probability for such a case to happen on the basis of the local vehicle traffic distribution and also the turning probability at an intersection. The detailed procedure of the protocol is presented. The SRR protocol is easy to implement and requires little extra routing information. Simulation results show that SRR can achieve high performance in terms of end‐to‐end packet delivery latency and delivery ratio when compared with existing protocols. Copyright © 2013 John Wiley & Sons, Ltd.

With the increasing popularity of wireless local area network infrastructure, Wi-Fi fingerprint based indoor positioning systems have received considerable attention in recent years. In the literature, most existing work in this area focuses on techniques that match the vector of radio signal strength (RSS) values reported by a mobile device to the fingerprints collected at predetermined reference points (RPs) by comparing the similarity (measured based on RSS difference) between them. However, these existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal distances in reality. To address this issue, in this paper, we propose a feature scaling based k-nearest neighbor algorithm (FS-kNN) for improved localization accuracy. In FS-kNN, we build a novel RSS-based feature scaling model, which introduces signal-level-scaled weights in the calculation of effective signal distance between signal vector reported by mobile device and existing fingerprints. Experimental results show that FS-kNN can achieve an average error distance as low as 1.93 meters, which is superior to previous work.

In this paper, we study the issue of mobility scheduling for mobile data collector (MDC) in wireless sensor networks. Most existing work in this area focuses on geometric-based optimization without considering the spatial correlation among different locations. In this paper, we study the mobility scheduling problem from the informative perspective by using Gaussian process to capture the spatial correlation of real world phenomena. Based on the Gaussian process model and collected sensing data from a number of sensor nodes in the network, one can predict the sensing values at the remaining interesting locations and can further estimate the prediction accuracy. This approach can potentially shorten the length of data collection tour with small penalty in data accuracy. We use the mutual information maximization criteria to evaluate the quality of a data collection tour. We accordingly formulate the informative mobility scheduling problem which finds the data collection tour with the maximal mutual information under certain mobility constraint. The problem is shown to be NP-hard and we accordingly propose two efficient heuristic algorithms. We evaluate the performance of our algorithms by comparing them with geometric-based algorithms through extensive simulations and the results show that our algorithms can return much shorter tours while achieving the same level of data quality.

In this paper, we design a novel lightweight ring-based routing protocol (LRRP) for wireless sensor network with mobile sinks (mWSN). The design objective is to significantly reduce the protocol overhead for route discovery and management while preserving high routing performance. For this purpose, LRRP builds a base ring by finding a shortest cycled path surrounding an artificially created topological hole in the network and, based on the base ring, it builds a ring-based structure to cover remaining nodes in the network and further assigns them ring IDs and virtual angles to ease the packet forwarding. LRRP works in a hybrid way for routing updates and packet forwarding. Specifically, each mobile sink (MS) dynamically chooses agent nodes, one on each ring, to proactively update its reachability as it moves. Each data packet is forwarded using ring-based forwarding along a pre-selected ring until reaching an MS or an agent node with path to an MS, from which the packet will be directly forwarded towards the MS. LRRP further considers how to achieve a good tradeoff between energy balancing among different rings and data path lengths. Extensive simulation results show that LRRP can significantly reduce the protocol overhead and achieve prolonged network lifetime as compared with existing work while achieving a very high packet delivery ratio.

Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement study, and service quality evaluation. However, due to limited access to large-scale VoD systems, there is still a lack of accurate model for characterizing the distribution of user watching time on a per video basis. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period, and characterize user watching time distributions of 1,000 most popular movies. We find that a video's watching time can be modeled by a concatenation of exponential distribution (in the first several minutes of the video) and truncated power law distribution (in the remaining time of the video), when users watch the video without interruptions. For comparison, user watching time with user interactions such as seeking and/or pause operations does not follow such a distribution. We further reveal interesting characteristics regarding the relation between video's watching time distribution and various watching/video-related features (including time-of-day, user ratings, and movie genres). Our measurement and modeling results bring forth important insights for design, deployment, and evaluation of Internet VoD systems.

In this paper, we propose a location-based friend-assisted coding-aware routing protocol (LFCR) for wireless multihop networks. To achieve improved network throughout, LFCR performs inter-flow network coding based routing with the assistance of location information. Specifically, LFCR combines friend-assisted path discovery and coding-aware routing. Further, when making decision on next hop selection, LFCR takes into account both coding opportunities and forwarding progress in next hop selection and attempts to make a good tradeoff between them. Simulation results show that LFCR significantly outperforms existing work in terms of network throughput, packet delivery ratio, and coding frequency.

Recently, much effort has been made for implementation of back-pressure scheduling in wireless networks. In this paper, we explore the implementation of back-pressure-based forwarding in wireless sensor networks. For this purpose, we propose Gradient-pressure, a practical Gradient-assisted anycast-based back-pressure framework for wireless sensor networks. Gradient-pressure introduces gradient information to assist transmission scheduling and realizes distributed anycast-based back-pressure scheduling on top of IEEE 802.11. Simulation results demonstrate that Gradient-pressure has high performance in terms of energy-use efficiency and goodput.

Localized topology control is attractive for generating reduced topologies with desirable features such as sparser connectivity and reduced transmit powers. In this paper, we propose an energy efficient localized topology control algorithm called X-LMST in order to achieve prolonged network lifetime. In X-LMST, each node is required to keep its one-hop neighborhood topology. Moreover, in X-LMST, a new metric is introduced for characterizing the energy criticality status of each link in the network. Each node independently constructs a local energy-efficient near-minimal spanning tree (MST) for finding a reduced neighbor set while maximally avoiding overusing energy-critical links in its one-hop neighborhood for future communications. Simulation results show that X-LMST significantly outperforms existing work in terms of network lifetime.

Network coding has been as a new coding paradigm that can significantly improve the throughput performance of a wireless multi-hop network. However, most previous studies either assume a fixed transmission power or do not take the impact of transmission power/rate to the network coding into consideration. Since in many scenarios, the selection of the transmission power/rate has big impact on network coding gains due to the fact the reception and overhearing probability, spatial reuse are both rely on transmission power/rate. Therefore, how to achieve high network throughput by appropriate selection of transmit power level and its corresponding packet transmit rate, network coding gain (if any) via localized network operations has been a critical issue in distributed multihop wireless networks, or alternatively, what is the best trade-off between the space-time resource (level of spatial reuse and transmission time) and the network coding gain? In this paper, our goal is to achieve the best trade-off between transmission power/rate and coding gains. Aiming at this, we propose a decentralized network coding aware power and rate control mechanism to enable each node to adjust its transmit power and data rate such that the network coding gains and the network throughput is maximized. Simulation results show that the proposed mechanism yields higher performance in network throughput as compared with existing work.

QoS routing plays a critical role in providing QoS support in the Internet. Most existing QoS routing algorithms employ the strategy of unidirectional search in route selection. Bidirectional search has been recognized as an effective strategy for fast route acquisition in identifying the shortest path connecting a pair of nodes. However, its efficiency has not been well established in the context of route selection subject to multiple additive constraints, which is in general NP-Complete. In this paper, we study how to employ bidirectional search to support efficient QoS routing subject to multiple additive constraints. The major contributions in this paper are as follows. First, we propose a$k$shortest path algorithm using bidirectional search, whose complexity is deduced to be$O(\sqrt{k}\vert V \vert\lg (\vert V \vert) + k\vert E\vert)$, where$\vert V \vert$and$\vert E \vert$represent the number of nodes and links in the network, respectively. Second, we show that bidirectional search can significantly accelerate the convergence of several existing QoS routing algorithms. Third, we propose a novel cost-effective bidirectional multi-constrained routing algorithm, which can greatly alleviate the forwarding state scalability issue by supporting stateless QoS routing in IP networks via IP tunneling or constraints-based alternate routing in MPLS networks via label stacks. It has the fastest known on-line running time$O(\vert V\vert)$. Theoretical and simulation results are given to demonstrate the high performance of our proposed algorithm in identifying QoS-satisfied paths and also in efficient resource utilization as compared with existing algorithms.

P2P systems require neighbor peers to frequently exchange buffer-map (BM) messages for efficient content sharing and distribution, which, however, can result in considerable communication overhead. A big problem in the BMs exchanged between neighbor peers is that a lot of information in them is redundant. To reduce the redundancy, some P2P systems have adopted certain block-level compression schemes (e.g., Huffman encoding) to compress each BM in isolation. However, these schemes simply treat each BM separately and as a single block of data, which largely affects their compression efficiency. In this paper, we propose a novel relevant-window-based (RW) compression framework, which takes advantage of the correlation between sequentially exchanged BMs between neighbor peers and thus can greatly remove the redundancy in them. We accordingly design a RW-based distributed compression scheme, which can work alone or co-work well with an existing block-level compression scheme for higher compression efficiency. We prove the correctness of our scheme and derive tight upper bound on average length of compressed bitmaps by our scheme via mathematical modeling. Numerical results demonstrate that our scheme alone can achieve compression efficiency of 96.6%, which can be further increased to up to 97.1% when jointly working with a block-level compression scheme.

A peer-to-peer (P2P) live streaming system faces a big challenge under flash crowds. When a flash crowd occurs, the sudden arrival of numerous peers may starve the upload capacity of the system, hurt its quality of service, and even cause system collapse. This paper provides a comprehensive study on the performance of P2P live streaming systems under flash crowds. By modeling the systems using a fluid model, we study the system capacity, peer startup latency, and system recovery time of systems with and without admission control for flash crowds, respectively. Our study demonstrates that, without admission control, a P2P live streaming system has limited capacity to handle flash crowds. We quantify this capacity by the largest flash crowd (measured in shock level) that the system can handle, and further find this capacity is independent of system initial state while decreasing as departure rate of stable peer increases, in a power-law relationship. We also establish the mathematical relationship of flash crowd size to the worst-case peer startup latency and system recovery time. For a system with admission control, we prove that it can recover stability under flash crowds of any sizes. Moreover, its worst-case peer startup latency and system recovery time increase logarithmically with the flash crowd size. Based on the analytical results, we present detailed flash crowd handling strategies, which can be used to achieve satisfying peer startup performance while keeping system stability in the presence of flash crowds under different circumstances .

ABSTRACT Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks (WSNs). However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine‐grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic‐generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out. To achieve high data quality and improved utilization of network resources, in this paper, we propose rate‐based adaptive precision setting (RAPS) protocol, which works in a way that each sensor can adaptively adjust its traffic‐generating rate on the basis of the current network resources availability and application requirements. RAPS introduces the following two key factors into its design: application's precision requirement and packet arrival rate. Analytical and simulation results show that RAPS can achieve improved data quality while reducing packet delivery latency. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose an energy-efficient on-demand multicast routing protocol (EMP) for wireless ad hoc and sensor networks. The design objective is to prolong the network lifetime of such networks. For this purpose, EMP introduces the strategy of energy critical avoidance in the process of ondemand construction of multicast routing trees. That is, those energy-critical nodes in the network are discouraged from in-volving a multicasting task. EMP also incorporates the destination-driven feature in its tree construction process in order to reduce the tree cost. We present the detailed design description of EMP. Simulation results show that EMP can achieve high performance in terms of network lifetime.

Low duty cycled scheduling can largely prolong the lifetime of a wireless sensor network (WSN) but also brings longer end-to-end (E2E) delivery delay. In this paper, our design objective is to pursue the near minimum E2E energy consumption subject to a desired success ratio that the E2E delay is below a delay bound. Accordingly, we design a Markov decision process based geographic routing protocol such that each relay node currently holding a packet makes localized forwarding decision on continuing waiting or transmitting immediately to the so far best forwarder candidate based only on local network state information. Simulation results show that the designed protocol can achieve expected success ratio subject to given delay bound and also high energy use efficiency.

In this paper, we propose an efficient network coding based back-pressure algorithm (NBP). NBP introduces the interflow network coding to improve the performance of the backpressure algorithm (a famous throughput-optimal cross-layer scheduling algorithm) for scheduling the transmissions of packets and also higher transmission efficiency. We theoretically prove that NBP can stabilize such networks. Simulation results demonstrate that NBP significantly outperforms traditional back-pressure algorithm in terms of packet delivery delay and average forwarding queue length.

Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement understanding, and system quality evaluation. However, due to the limited access of user data in large-scale streaming systems, a systematic measurement, analysis, and modeling of video watching time is still missing. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period. We collect accurate user watching data of more than 100 million streaming sessions of more than 100 thousand distinct videos. Based on the measurement data, we characterize the distribution of watching time of different types of videos and reveal a number of interesting characteristics regarding the relation between video watching time and various video-related features (including video type, duration, and popularity). We further build a suite of mathematical models for characterizing these relationships. Extensive performance evaluation shows the high accuracy of these models as compared with commonly used data-mining based models. Our measurement and modeling results bring forth important insights for simulation, design, deployment, and evaluation of Internet VoD systems.

ABSTRACT In this paper, we study the one‐dimensional coverage problem in a wireless sensor network (WSN) and consider a network deployed along a one‐dimensional line according to a Poisson distribution. We analyze three important parameters that are related to the problem, i.e.,expected k‐coverage proportion, full k‐coverage probability, and partial k‐coverage probability, and derive mathematical models that describe the relationships between the node density in the network and these parameters. The purpose is to calculate or estimate the node density required for achieving a given coverage probability, which is useful in the deployment of a one‐dimensional network for many applications. We first analyze the expectedk‐coverage proportion, then analyze the fullk‐coverage probability fork = 1 and the lower bound to the fullk‐coverage probability fork > 1, and finally analyze the partialk‐coverage probability fork = 1 and give a brief discussion of the partialk‐coverage probability fork > 1. The mathematical models are validated through simulation. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.

Low duty cycled sleep scheduling can largely prolong the lifetime of a wireless sensor network (WSN). However, it also brings big challenge to the design of efficient routing protocols. Inappropriate selection of routes can largely offset the efficiency of a sleep scheduling mechanism. This paper aims to provide a simple but efficient forwarding discipline for packet routing in a low duty cycled WSN wherein each node chooses its wakeup slot in a pseudo-random manner. In such a duty-cycled WSN, a big problem is how a node can make a forwarding decision in order to obtain an acceptable end-to-end (E2E) delivery latency as well as high energy efficiency. In this paper, we design a multipath routing protocol that combines gradient information and predictable link delay to make the forwarding decision. Simulation results show that the designed protocol can obtain high performance in terms of delivery latency and energy efficiency compared with existing work.

This article proposes a destination-driven on-demand multicast routing protocol (D-ODMRP) to improve the multicast forwarding efficiency in mobile ad hoc networks (MANETs). In D-ODMRP, the path from the multicast source to a multicast destination tends to use those paths passing through another multicast destination. If such multiple paths are available, the one leading to the least extra cost is preferred. This destination-driven strategy is introduced into the on-demand construction process of a multicast forwarding structure in a popular multicast protocol ODMRP. Simulation results show that D-ODMRP can significantly improve the forwarding efficiency as compared with ODMRP. Moreover, the destination-driven strategy can also be introduced into other existing multicast routing protocols for MANETs.

ABSTRACT Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre‐determined path. This property makes it suitable for WSN applications in which data‐collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range‐free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract In‐network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy‐efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd.

In a wireless sensor network with mobile sinks, the maintenance of efficient data delivery structure often brings a large amount of control overhead, which may offset the benefit of introducing mobile sinks. In this paper, we propose a multi-stage data routing protocol (called MLRP) to address this problem. To reduce the protocol overhead, MLRP integrates layered Voronoi scoping and dynamic anchor selection. Such design is expected to greatly reduce the diffusion scope of sink movement updates and thus reduce the frequency at which the data delivery structure is refreshed. Simulation results show that MLRP can effectively reduce the protocol overhead while ensuring high packet delivery ratio as compared with existing work.

A fundamental problem that a peer-to-peer (P2P) live streaming system faces is how to support flash crowds effectively. A flash crowd occurs when a burst of join requests arrive at a system. When a flash crowd occurs, the sudden arrival of numerous peers may starve the upload capacity of a P2P system, and degrade the quality of service. By theoretical analysis and simulations, we find that a system has limited capacity to handle a flash crowd: It can recover to a new stable state when the size of flash crowd is small or moderate, but collapse when the flash crowd is excessively large. The capacity of a system is independent of initial state of the system while relevant to stable peers' departure rate, which suggests this capacity is an essential property of a P2P live streaming system. In addition, we prove that a P2P live streaming system with admission control has excellent capacity to handle flash crowds: It can recover from flash crowds of excessively large size and a startup peer's waiting time scales logarithmically with the size of flash crowds. Our theoretical model and simulation results provide a promising framework to understand the capacity of a P2P live streaming system for handling flash crowds.

In this paper, we propose a data-driven routing protocol (called DDRP) for wireless sensor networks with mobile sinks (mWSNs). The design objective of DDRP is to effectively reduce the protocol overhead for data gathering in such networks. DDRP exploits the broadcast feature of wireless transmissions for sensor nodes for (gratuitous) route learning. To achieve this goal, each data packet carries an additional option recording the known distance from the sender of the packet to the destined mobile sink. The overhearing of such a data packet will gratuitously provide listeners a route to mobile sink. This is the so-called data-driven nature of DDRP. Continuous such route-learning among neighboring nodes will provide route information for more and more sensor nodes in the network. We present the detailed design of the DDRP protocol. Simulation results show that DDRP has much lower protocol overhead as compared with existing work while ensuring high packet delivery ratio.

In a peer-to-peer (P2P) live streaming system, peer requests collide when multiple peers request data pieces from the same peer (or media server). When collisions occur, some of the peers' requests fail and retries have to be taken, which delays peer's receipts of pieces. This paper shows that request collisions occur frequently at both media servers and peers, and have big impact on system performance. It then proposes two algorithms to address this issue. The first is a novel admission control algorithm at the media server. The second is a peer selection algorithm in which peer requests pieces from neighbors with low collision probability. Simulation results show that the proposed algorithms improve system performance significantly.

In wireless sensor networks (WSNs), a sensor node may not always be able to report all its readings to the sink node because of limited network resources. Thus, it is desirable to have an efficient data reporting strategy with high accuracy for data reporting in such networks. In this study, the authors study data reporting in a WSN with no a priori information on future sensor readings and analyse the rationale behind the widely used equi-interval data reporting strategy in terms of the accuracy in data collection. To support the equi-interval data reporting strategy, the authors propose an adaptive rate control algorithm to achieve the maximum data reporting rate under the bandwidth constraint, and further extend this algorithm to one that can adaptively adjust the reporting rate of a sensor based on the residual energy of the sensor in order to prolong the network lifetime. Simulation results show that the equi-interval data reporting strategy can achieve higher accuracy than other strategies and with the proposed rate control algorithms it can further improve the network performance in terms of data accuracy and network lifetime.

The authors propose a new geographic hole-bypassing forwarding (HBF) protocol to address the hole diffusion problem in wireless sensor networks (WSNs). To support efficient hole-bypassing, the HBF protocol models a hole using a virtual circle whose radius is adjustable within a certain range and is calculated on a per-packet basis. The information associated with the virtual circle will be used, if needed, for selecting an anchor point to bypass the hole in order for a packet to reach a particular sink node. The design objective of the HBF protocol is to balance the traffic load among the nodes near an actual hole boundary. Using the HBF protocol, a packet is always sent to the closest sink and the extra distance for hole-bypassing is considered in the delivery of data packets to reach the sinks (or some of them) in the network. The simulation results show that HBF outperforms existing hole-bypassing protocols in terms of packet delivery ratio and network lifetime.

Wireless sensor networks with mobile sinks (mWSN) have attracted a lot of attention recently. In an mWSN, each mobile sink can move freely and unpredictably. In this paper, we design two efficient data gathering protocols for mWSNs. The first protocol (called AVRP) adopts Voronoi scoping plus dynamic anchor selection to handle the sink mobility issue. In the second protocol (called TRAIL), the trail of mobile sink is used for guiding packet forwarding as sinks move in the network. In TRAIL, to forward a data packet, integration of trail-based forwarding and random walk is used. Specifically, when no fresh trail of any sink is known, random walk is used; once a sensor on a fresh sink trail is reached, data packet will be forwarded along the trail. TRAIL is simple to implement and has small protocol overhead. Simulation results show the designed protocols have high performance and further AVRP is suitable for mWSNs with heavy traffic while TRAIL is suitable for mWSNs with light traffic.

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

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

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

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

This special issue includes a collection of 19 outstanding research papers which cover a diversity of topics on the application of network coding in wireless communication networks.

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

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

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

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

Energy-efficient use is a critical issue in the design of wireless multi-hop networks such as wireless ad hoc and sensor networks. Location-based routing protocols are known to have high efficiency, robustness, and scalability and are suitable to be deployed in wireless multi-hop networks. In this paper, we present the design of an energy-efficient localized geographic forwarding algorithm. The design objective is to prolong the lifetime of wireless multi-hop networks. To achieve this goal, our algorithm design employs the strategies of localized implementation of Dijkstra's algorithm and energy criticality avoidance when making decisions on next hop selections for packets forwarding. Simulation results demonstrate that our designed algorithm can achieve high performance in terms of network lifetime.

The ocean, which covers about two-third of the Earth surface, is a largely unexplored world that has fascinated humans since the beginning of human history. Over a long period of time, there is a great interest in exploring the ocean and other underwater environments (e.g., rivers, lakes, and reservoirs) for scientific, environmental, commercial, and military purposes. With the increasing demand for acquiring localized, precise and real-time knowledge of the harsh underwater environments, traditional underwater exploration technologies such as SONAR or other remote sensing technologies can no longer meet such demands. Underwater sensor networks are an emerging network paradigm which provides a promising solution to exploring the ocean and underwater environments. An underwater sensor network consists of a number of underwater sensor nodes with sensing, data processing, and communication capabilities, which are deployed in a region of interest and collaborate to accomplish a common task such as underwater environmental monitoring, mine reconnaissance, and military surveillance. Driven by a broad range of potential applications in both civilian and military areas as well as rapid technological advances in microelectronics, wireless communications, and embedded processing, underwater sensor networks have recently received much attention from both academia and industry. Distinct from terrestrial sensor networks, an underwater sensor network has some unique characteristics that need to be particularly addressed such as low communication bandwidth, large propagation delay, harsh geographical environment, and floating node mobility. These unique characteristics present many challenges in the design of underwater sensor networks, which have recently motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of eight outstanding research papers, which cover a diversity of topics on the design of network architectures and protocols for underwater sensor networks. The issue begins with an invited paper, ‘Prospects and Problems of Wireless Communication for Underwater Sensor Networks,’ contributed by Jun-Hong Cui et al. This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communications using physical waves as the carrier among nodes in an underwater sensor network. It also makes recommendations for the selection of the communication carrier for underwater sensor networks with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. In the second paper, ‘Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks,’ Alam and Haas studied the node deployment problem in a 3D underwater sensor network and provided a solution to the coverage and connectivity problem with limited and full communication redundancy requirements. In the third paper, ‘Placement of Multiple Mobile Data Collectors in Underwater Acoustic Sensor Networks,’ Alsalih et al. studied the placement problem of mobile data collectors in underwater sensor networks and proposed two routing and placement schemes. One is delay-tolerant placement and routing (DTPR), which can maximize the network lifetime without any delay consideration. The other is delay-constrained placement and routing (DCPR), which can maximize the network lifetime with an upper bound on the maximum delay. The fourth paper, ‘Target Tracking Based on a Distributed Particle Filter in Underwater Sensor Networks,’ by Huang et al. proposes two algorithms for tracking mobile targets in cluster-based underwater sensor networks based on a distributed particle filter. One of them can achieve higher tracking accuracy while the other can significantly reduce the communication cost, energy cost, and tracking response time. In the fifth paper, ‘Utilizing Acoustic Propagation Delay to Design MAC Protocols for Underwater Wireless Sensor Networks,’ Guo et al. proposed an efficient MAC protocol for underwater sensor networks, which makes use of the propagation delay to avoid collisions, thus reducing control overhead and energy consumption. In the sixth paper, ‘Path Unaware Layered Routing Protocol (PULRP) With Non-Uniform Node Distribution for Underwater Sensor Networks,’ Gopi et al. proposed a PULRP for 2D underwater sensor networks with mobile nodes, which has been demonstrated to have better throughput and delay performance as compared to the underwater diffusion (UWD) algorithm. In the seventh paper, ‘PAS: Probability and Sub-Optimal Distance (SOD)-Based Lifetime Prolonging Strategy for Underwater Acoustic Sensor Networks,’ Dou et al. proposed a couple of lifetime prolonging strategies for underwater sensor networks: probability-based energy-balancing (PEB) strategy and SOD-based data transmission strategy. They showed through simulation results that both strategies can efficiently save energy consumption and thus prolong the network lifetime. In the last paper, ‘Development of Routing Protocols for the Solar-Powered Autonomous Underwater Vehicle (SAUV) Platform,’ Bartos et al. presented a summary of the experience obtained in the development, evaluation, and field testing of two routing protocols for the SAUV platform. Useful suggestions based on field experience are also presented for improving the design and evaluation of routing protocols for a harsh underwater environment. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can include only eight papers in the issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process. It is our hope that the papers included in this special issue present a good snapshot of the latest research progress in the design of network architectures and protocols for underwater sensor networks and become an important reference for researchers and practitioners in the area. Finally, we hope that the readers will find this special issue timely and informative.

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

Scalability has been a crucial design concern for quality of service routing protocols to be deployed in high-speed communications networks. The issue of bandwidth-constrained widest-shortest path (WSP) routing, which selects the WSP connecting a pair of nodes subject to a bandwidth constraint is studied. The design objective is to enable fast route calculation in identifying such constrained paths. To achieve this goal, a polynomial optimal algorithm using bidirectional search is designed. The complexity of the designed algorithm is deduced to be O(|E|lg|V|), where |E| and |V| represent the number of links and nodes in the network, respectively. Simulation results demonstrate that the designed algorithm can significantly reduce the average-case computational overhead caused by the calculation of such constrained routes as compared with related work.

In this paper, we study the issue of dynamically selecting shortest paths in wavelength-routed networks. We present several fast shortest path selection algorithms for networks with and without wavelength conversions. The presented algorithms employ the strategies of sequential search, backward routing, and informed search. Simulation results demonstrate that our presented algorithms can significantly reduce the average-case running time in identifying shortest paths in wavelength-routed networks.

In this paper, we design grid-based routing (GBR) protocols for wireless multi-hop networks. The objective is to effectively reduce the protocol overhead for network management with the assistance of position information. GBR divides networks into equally spaced grids. To perform a routing operation, GBR requires as few grids as possible to participate while preserving network connectivity. We design different protocols for different environments and deduce analytical results to observe the high performance of the designed protocols.

Power use is a crucial issue in wireless ad hoc networks since mobile hosts are typically battery-constrained. This paper presents a localized power-efficient routing protocol, which aims at improving the power-use efficiency of traditional ad hoc routing protocols. Our protocol works by introducing recursive localized power-use optimization at intermediate nodes on routes that a traditional routing protocol returns either proactively or reactively. For this purpose, each network node maintains the state information of its one-hop neighborhood. Simulation results demonstrate that the designed protocol can significantly improve the power use efficiency of traditional protocols. The simplicity, low overhead, and high efficiency in power utilization make the designed protocol suitable for providing scalable power-efficient routing support in wireless ad hoc networks.

This paper presents the design of a position-aided on demand routing (PAR) protocol. The objective is to effectively reduce the communication overhead associated with path discovery and maintenance with the assistance of position information. For this purpose, PAR employs a novel restricted directional flooding mechanism. This mechanism creates an ellipse-forwarding zone, across which control messages are propagated for route discovery. Moreover, PAR uses location-guided expanding ring search, which works by searching successively larger areas for paths. This searching strategy can effectively prevent unnecessary network-wide flooding. In terms of location tracking, we design a method of recursive passive listening at intermediate nodes for end-to-end location tracking. This method enables the source to effectively keep track of the up-to-date location of its communication partner at little control overhead. Simulation results demonstrate that the PAR protocol can significantly reduce the communication overhead associated with path discovery, compared with related work.

This paper proposes a multi-initiation mechanism for dynamic path restoration to handle single-link failures in GMPLS-based WDM networks. This mechanism allows multiple network nodes on the primary path of a disrupted connection to participate in the restoration of the disrupted connection. Each of the nodes respectively initiates a restoration process upon the detection or notification of a link failure. In each of the processes, the initiating node attempts to dynamically establish a backup path for the disrupted connection. The destination node acts as a coordinator among multiple restoration processes. The purpose is to reduce the path restoration time so that a backup path can be provisioned more quickly for each disrupted connection that traverses a failed link. Based on this mechanism, a path restoration protocol is then presented and the performance of the protocol is evaluated through simulation experiments in terms of path restoration time and path restoration probability.

Energy use is a crucial design concern in wireless ad hoc networks. The design objectives of energy-aware routing include selecting energy-efficient paths and minimizing the protocol overhead incurred in acquiring such paths. To achieve these goals altogether, we present the design of two energy-aware on-demand routing protocols for different network environments. The key idea behind our design is to adaptively select the subset of nodes required to involve in a route-searching process to acquire a high residual-energy path or the degree to which nodes are required to participate in the process of searching for a low-power path for networks wherein nodes can adaptively adjust their transmission power: Analytical and simulation results are given to demonstrate the high performance of the designed protocols in energy-efficient utilization and in reducing the protocol overhead incurred in acquiring energy-aware routes.

In this work, we design a destination-initiated protocol, which aims at building source-rooted shortest path tree (SPT) in a hop-by-hop manner for providing scalable multicast. The designed protocol can support group applications with dynamic membership well. Performance analysis and simulation results show that the destination-initiated characteristic can significantly reduce the computation, storage and communication overhead associated with SPT constructions.

The multicast forwarding state scalability issue is one of the critical issues that delay the deployment of IP multicast in the global Internet. With traditional protocols, each router is required to maintain a forwarding entry locally for each group whose distribution tree passes through the router itself. Consequently, the number of forwarding entries at routers increases linearly with the number of concurrent ongoing multicast sessions. This can pose the forwarding state scalability issue when the number of multicast sessions is very large. The paper addresses this scalability issue in providing efficient delay-constrained multicasting in IP networks. We propose a scalable multicast routing heuristic. Its computational complexity is deduced to be O(m|V|/sup 2/), where m is the size of the multicast group and |V| is the size of the network. In particular, if the heuristic is executed online, its computational complexity can be further reduced to O(m/sup 2/). This property makes the heuristic scale well with the number of concurrent sessions since multicasting subject to a delay constraint is typically executed on a per-session basis. Simulation results show that the proposed heuristic can achieve high performance in reducing the forwarding state at routers and in utilizing network resources efficiently.

One of the key issues in QoS provisioning in high-speed networks is how to determine a feasible route that satisfies the given QOS requirements while efficiently utilizing network resources. In this paper, we study the NP-complete problem of path selection subject to multiple constraints and propose a heuristic solution, which essentially divides an entire QoS-path into at most two "superedges" that is connected by a "relay node". A superedge is defined as a connected segment of the path on which all routers use the same routing metric for packet forwarding. The node connecting the two superedges is called relay node. This property makes the heuristic be able to support stateless forwarding, i.e., no flow-specific state information is required to maintained at intermediated nodes on a QoS routing protocol. Its computational complexity is deduced to be 0(m|V/sup 2/|), where m, a very small integer, is the number of the concerned QoS metrics and |V| is the number of nodes in network. Simulation results show that the heuristic can achieve near optimal performance.

We extend an existing multicast routing protocol, multicast extension to OSPF (MOSPF), to achieve tunnel multicasting. The extension is as follows. Tunneling is introduced for supporting tunnel multicasting, which aims at reducing the protocol overhead associated with MOSPF. Simulation results show that the extension can reduce protocol overhead significantly without affecting the per-destination shortest path characteristics of a resulting tree or introducing any extra control overhead.

Shortest path tree (SPT) is the most widely-used multicast tree type due to its simplicity and low per-destination cost. An SPT is constructed by the union of the shortest paths from the source node to each destination. However, SPT does not consider overall network resource utilization. We propose a destination-driven shortest path tree algorithm, which aims to construct a low-cost SPT by considering link sharing between different destinations. The computational complexity of the presented algorithm is O(|E|log|V|), where |E| and |V| are the number of edges and nodes in a network respectively. Simulation results are used to demonstrate the high performance of the proposed algorithm.

QoS routing has generally been addressed in the context of reservation-based network services (e.g. ATM, IntServ), which require explicit (out of band) signaling of reservation requests and maintenance of per-flow state information. It has been recognized that the processing of per-flow state information poses scalability problems, especially at core routers. To remedy this situation, in this paper we introduce an approach for stateless QoS routing in IP networks that assumes no support for signaling or reservation from the network. Simple heuristics are proposed to identify a low-cost delay-constrained path. These heuristics essentially divide the end-to-end path into at most two "superedges" that are connected by a "relay node". Routers that lie on the same superedge use either the cost metric or the delay metric (but not both) to forward the packet. Simulations are presented to evaluate the cost performance of the proposed approach.

In this paper, we propose a fast multicast routing heuristic, called DCMA, for delay-sensitive applications. The running complexity of DCMA is O(m log m+|V|), where m is the size of the multicast group and |V| is the number of nodes in the network. We first present a unicast version of DCMA, called SDCR, which is used to setup an end-to-end, cost effective delay-constrained path between two nodes. SDCR can always find a delay-constrained path if one exists. Its running complexity is O(|V|). SDCR is used in constructing DCMA, which is guaranteed to be loop free. Simulation results are used to demonstrate the high performance and cost effectiveness of the proposed heuristic.

The integrated services (Intserv) architecture provides the Internet the ability of delivering end-to-end QoS to applications over heterogeneous networks. Existing approaches for providing Intserv require routers to manage per flow states and perform per flow operations. Such a stateful network raises the scalability concerns when the network size or the number of flows is significantly large. The differentiated services (Diffserv) approach proposes a scalable means to deliver IP QoS based on aggregate traffic handling. We present end-to-end QoS guaranteed services over Diffserv network. This is implemented by introducing an sender-initiated resource reservation mechanism over Diffserv. This way we simultaneously achieve scalability and better control on the services. We present a detailed experimental study of the end-to-end QoS behaviour. Our results demonstrate that it is very desirable to implement the reservation mechanism over Diffserv to achieve more flexible and efficient end-to-end guaranteed QoS.