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This article studies the mean standard deviation (mean-std) Canadian traveller problem (CTP). Different from the canonical CTP, which aims at minimizing the traveller's expected travel time, while considering edge breakdown probabilities, we introduce the reliability version of CTP, which tries to find a routing policy with the minimal linear combination of the travel time's mean and standard deviation. With the recent development of internet-of-things (IoT) technology, the transportation network's edges' travel-time statistics, i.e., mean and standard deviation, as well as the traversal probabilities, are available to the end users. With those information, we propose a dual dynamic programming (DDP) method, which simultaneously estimates the first-order and the second-order moments of a given decision-list (DL) policy, and thereby makes improvements towards to the optimal one through the generalized policy iteration (GPI) scheme. We construct an open source benchmark environment to evaluate the performance of different mean-std CTP solutions, and show that the DDP method outperforms state of the arts in a range of transportation networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed joint sensing and communication (DJSC) system in which each radar sensor as a JSC node is equipped with a sensing function and a communication function. Deploying multiple JSC nodes may require a large amount of bandwidth. Therefore, we investigate the bandwidth allocation problem for the DJSC system. In particular, we aim to optimize the bandwidth allocation to the sensing function and the communication function of the JSC nodes. To improve the allocation efficiency while benefiting the spatial diversity advantage of the DJSC systems, the objective is to maximize the sum of sensing performances, i.e., estimation rates, communication performances, i.e., communication data rates, and fairness of all the users. The optimization problem is non-convex and difficult to be solved. For this, we propose a fully polynomial time approximation algorithm, and we prove that the approximation algorithm can guarantee a near-optimal solution with an accuracy bound of $\epsilon$. Furthermore, we propose to use a heuristic algorithm with lower complexity. The simulation results show that both the proposed algorithms are able to achieve the solutions close to the optimum in a computationally efficient fashion. [ABSTRACT FROM AUTHOR]

In opportunistic mobile crowdsensing, the objective of service requesters is to have as many of their sensing tasks completed as possible within their budget constraints, whereas that of participants (workers) is to collect the highest monetary reward possible on their trajectories. However, these objectives can conflict and may result in unhappy service requesters or workers if the matching between them is not handled carefully. In this paper, we study the problem of finding task assignments that fulfill both coverage-aware preferences of service requesters and profit-based preferences of workers in a budget-constrained, opportunistic mobile crowdsensing system. Since this is a matching problem with bilateral preferences, we aim to find a matching in which everyone is satisfied with their assignment based on their preference profile. We first propose a polynomial-time approximation algorithm for general settings, and then show that a slightly modified version of this algorithm has a constant approximation ratio when the rewards offered to workers by service requesters are proportional to the coverage capability of workers for corresponding tasks. Through extensive simulations, we evaluate the performance of our algorithms in different settings, and show that they mostly provide substantially better task assignments in terms of user happiness and coverage quality while having a few orders of magnitude lower running times compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is assisted by a mobile UAV base station, which caches some popular contents for wireless backhaul link traffic offloading. In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance. To cope with the dynamic UAV locations and content requests in practical scenarios, we formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP). The UAV acts as an agent to take actions for caching placement and resource allocation, which includes the user scheduling of content requests and the power allocation of NOMA users. In order to tackle the MDP, we propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with soft ε-greedy strategy to search for the optimal match between actions and states. Since the action-state table size of Q-learning grows with the number of states in the dynamic networks, we propose a function approximation based algorithm with combination of stochastic gradient descent and deep neural networks, which is suitable for large-scale networks. Finally, the numerical results show that the proposed algorithms provide considerable performance compared to benchmark algorithms, and obtain a trade-off between network performance and calculation complexity. [ABSTRACT FROM AUTHOR]

Recent years have witnessed increasingly more uses of Unmanned Aerial Vehicle (UAV) swarms for rapidly providing wireless coverage to ground users. Each UAV is constrained in its energy storage and wireless coverage, and it consumes most energy on flying to the top of the target area, thus leaving limited leftover energy for hovering at its deployed position and keeping wireless coverage. The literature largely overlooks the sustainability issues of deploying UAV swarm and the UAV-to-UAV cooperation, and we aim to maximize the minimum leftover energy storage among all the UAVs after their deployment. Our new energy-saving deployment problem captures that each UAV's wireless coverage is adjustable by its service altitude, and also takes the no-fly-zone (NFZ) constraint into account. We show that a UAV with larger initial energy storage in the UAV swarm should be deployed further away from the UAV station, and we propose an optimal energy-saving deployment algorithm by jointly balancing heterogeneous UAVs’ flying distances on the ground and final service altitudes in the sky. When n UAVs with different initial energy storages are dispatched from different initial locations, we prove this problem is NP-hard in general by reduction from partition problem. Despite of this, we propose to preserve the UAVs’ location order and successfully design an approximation algorithm of complexity n log 1/ε to arbitrarily approach the optimum with error ε. Inspiring by this proposed (1−ε)-approximation algorithm, we present a heuristic algorithm to solve the general case by balancing the efficiency and computation complexity well. [ABSTRACT FROM AUTHOR]

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Location awareness is becoming essential for various wireless applications, and cooperation among nodes is highly beneficial for localization performance. However, the existing cooperative localization (CL) algorithms may suffer from major performance loss when applied to the mobile environments with fast changing network topologies. In this paper, we extend the joint spatial and temporal cooperation (JSTC) model to the range-based localization problem. Based on mobility constraints and the maximum likelihood estimation (MLE) criterion, we formulate the CL problems in both centralized and distributed cases. Different from the conventional distributed CL problems, the proposed one takes the transmission delay and the cooperation among multi-hop nodes into account, which is more practical. Moreover, the performance limits of the considered problems are investigated by the Cramér-Rao lower bound (CRLB). Finally, these two problems are solved by the Sequential Quadratic Programming (SQP) algorithm. Both simulation and experiment results show that the extended JSTC model can effectively improve the localization reliability and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we investigate non-cooperative radio resource management in a vehicle-to-vehicle communication network. The technical challenges lie in high-vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued data packets, aiming to optimize the expected long-term performance. The frequency allocation at the beginning of each slot at the RSU is regulated by a sealed second-price auction. Such interactions among VUE-pairs are modeled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the original game into an equivalent stochastic game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium, which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE solution. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm. [ABSTRACT FROM AUTHOR]

We consider the problem of link scheduling in multihop wireless networks subject to interference constraints. We consider a graph-based representation of wireless networks where scheduled links adhere to the $K$ -hop link interference model. For this scheduling problem, we develop an asynchronous and distributed greedy heuristic. Furthermore, we show that this distributed heuristic computes the exact same schedule as the well-known centralized greedy link scheduling algorithm. [ABSTRACT FROM AUTHOR]

Optimal road side unit (RSU) deployment is of crucial importance to vehicular ad hoc networks. The RSU deployment (RD) problem models in the literature usually lack the ability to describe curve-shaped roads and roads nonuniform statistics. We had proposed a more realistic RD problem model taking these features into consideration. However, our understanding of the RD problem with the new model is still seriously limited. In this paper, we try to deepen our understanding of the one-dimensional RD (D1RD) problem with the new model by analyzing its properties and designing optimal and efficient approximate algorithms for it. We first analyze the properties of optimal solutions of the D1RD problem. Next, suspecting that the D1RD problem with $n$ RSUs of different coverage radii is intractable, we propose two greedy-based algorithms (named as Greedy2P3 and Greedy2P3E) and show that Greedy2P3E's approximation ratio is at least $1-(\frac{n-1}{n})^2$. Then, by exploiting the properties of the D1RD problem, we propose an optimal algorithm named OptGreDyn by combining greedy idea and dynamic programming, and prove its optimality. At last, we proved that, if applied to the RD problem with $n$ RSUs of identical radii, the approximation ratios of Greedy2P3 and Greedy2P3E are at least $\frac{2}{3}$ , and it is tight for Greedy2P3 when $n=3i$ , where $i$ is a positive integer. Simulation results verify the optimality of OptGreDyn, meanwhile show that Greedy2P3 and Greedy2P3E usually return near-optimal solutions with profit more than 98% of the optimal solutions, and they are preferable than the existing approximate algorithms. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization in the world, urban traffic congestion has become an urgent challenge in most cities. Adaptive traffic signal control is the most approved control method to solve the problem, and accurate real-time traffic information is critical to this solution. This paper presents distributed cooperative reinforcement learning-based traffic control that integrates V2X networks’ dynamic clustering algorithm. To obtain traffic flow information accurately and instantaneously, it is important to improve the cluster stability in V2X networks. A dynamic clustering algorithm is proposed based on the enhanced affinity propagation. The proposed clustering algorithm introduces the initial cluster partition to maintain a proper cluster size and adds the lane and destination factors to improve the cluster's stability. The algorithm can provide efficient and accurate traffic state information to traffic signal controls. By integrating the clustering algorithm, a cooperative reinforcement learning control scheme is proposed to balance the traffic load. To address the tough dimensionality curse of reinforcement learning, a distributed mechanism for intersection cooperation is introduced, and a fast gradient-descent function approximation method is proposed to improve the controls’ real-time performance. The proposed intelligent traffic control scheme that integrates the stable clustering algorithm can effectively improve the traffic throughput, reduce the average waiting time, and avoid congestion. Numerical simulations on real scenarios validate the performance of the proposed approach. [ABSTRACT FROM PUBLISHER]

Channel rendezvous is a vital step to form a cognitive radio network (CRN). It is intractable to guarantee rendezvous for secondary users (SUs) within a short finite time in asynchronous, heterogeneous, and anonymous CRNs. However, most previous heterogeneous algorithms rely on explicit SUs' identifiers (IDs) to guide rendezvous, which is not fully distributed. In this paper, we exploit the mathematical construction of sunflower sets to develop a single-radio sunflower set (SSS)-based pairwise rendezvous algorithm. We propose an approximation algorithm to construct disjoint sunflower sets. Then, the SSS leverages the variant permutations of elements in sunflower sets to adjust the order of accessing channels instead of SUs' IDs, which is more favorable for anonymous SUs in distributed environments. We also propose a multiradio-sunflower-set-based pairwise rendezvous algorithm to bring additional rendezvous diversity and accelerate the rendezvous process. Moreover, for the case with more than two SUs, we propose a multiuser collaborative scheme in which SUs cooperatively exchange and update their channel-hopping sequences until rendezvous. We derive the theoretical upper and lower bounds of rendezvous latency of the proposed algorithms. Extensive simulation comparisons with the state-of-the-art blind-rendezvous algorithms are conducted, incorporating the metrics of maximum and expected time-to-rendezvous. The simulation results show that our algorithms can achieve rendezvous faster than previous works. [ABSTRACT FROM PUBLISHER]

In this paper, we study the deployment of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period so that none of the sensors will run out of energy, where sensors can be charged by the charging vehicles with wireless energy transfer. To minimize the network operational cost, we first formulate a charging scheduling problem of dispatching multiple mobile charging vehicles to collaboratively charge sensors such that the sum of travelling distance (referred to as the service cost) of these vehicles for this monitoring period is minimized, subject to that none of the sensors will run out of energy. Due to NP-hardness of the problem, we then propose a novel approximation algorithm with a guaranteed approximation ratio, assuming that the energy consumption rate of each sensor does not change for the given monitoring period. Otherwise, we devise a heuristic algorithm through modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via experimental simulations. Simulation results show that the proposed algorithms are very promising, which can reduce the service cost by up to 20% in comparison with the service costs delivered by existing ones. [ABSTRACT FROM PUBLISHER]

Numerous approaches have been proposed to solve shortest path query problems (SPQPs) in either static or time-dependent spatial networks; however, these approaches are neither appropriately nor efficiently used to find the nearest shelter with fastest paths in disaster evacuations. In disasters, segments of a path computed and saved as the fastest might become impassable. The nearest shelter differs for people depending on their locations and can also change on the basis of an unpredictable and highly dynamic edge cost (e.g., maximum passable vehicle speed), which is influenced by disaster events. To solve this problem, this paper proposes a dynamic network model, which is called an event-dependent network, to represent a spatial network in a disaster. Effective approaches using multiple algorithms are proposed for on-the-fly computation of the nearest shelter and fastest paths in a disaster. A distributed system consisting of a server and multiple mobile clients using our approaches is presented for navigating the fastest paths for people to evacuate a disaster area. Real-world maps, such as a map of California, were used in our experiments. The results revealed that our approaches require less than 2 ms to find a new nearest shelter and its fastest paths, which is faster than other approaches for solving the fastest-path problem. [ABSTRACT FROM AUTHOR]

In this paper, we model a vehicular multihop network as an evolving graph and formulate the problem of optimal data dissemination over the network in terms of minimum number of transmissions. We show that the problem belongs to the NP complexity class and provide an easy to implement polynomial-time \2 \tau H(\Delta)-approximation algorithm, where \tau is the number of different subgraphs that comprise the evolving graph, and H(\Delta) is the harmonic number of the degree of the evolving graph. By means of applying our heuristic over a vehicular scenario generated by a microscopic road-traffic simulator, we provide some insight into the data dissemination issue. In addition, the proposed algorithm is employed to benchmark a state-of-the-art communication protocol. We hope this paper will inspire more efficient heuristics and data dissemination solutions. [ABSTRACT FROM PUBLISHER]

Opportunistic scheduling algorithms in cognitive radio networks (CRNs) allocate resources by exploiting the variations in channel conditions and spectral activities of primary users. However, most of these scheduling algorithms ignore the per-user throughput requirements. In this paper, we formulate a scheduling problem called maximizing the number of satisfied users (MNSU), which maximizes the number of secondary users that are satisfied in terms of throughput in a centralized CRN. We show that MNSU is NP-hard in the strong sense and cannot be approximated within any constant factor better than 2 unless P = NP. We also prove that MNSU is at least as hard as the max–min fair scheduling problem, which has previously been proven to be a computationally very difficult problem in the literature. We then propose two heuristic algorithms: 1) best first resource assignment and 2) resource assignment with partial backtracking. We demonstrate that our proposed algorithms yield high performance while still achieving low computational complexity. [ABSTRACT FROM PUBLISHER]

Cooperative communication (CC) offers an efficient and low-cost way to achieve spatial diversity by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of CC. Most existing relay selection strategies focus on optimizing the outage probability or energy consumption. To fill in the vacancy of research on throughput improvement via CC, we study the relay selection problem with the objective of optimizing the throughput in this paper. For unicast, it is a P problem, and an optimal relay selection algorithm is provided with a correctness proof. For broadcast, we show the challenge of relay selection by proving it nonprobabilistic hard (NP-hard). A greedy heuristic algorithm is proposed to effectively choose a set of relay nodes that maximize the broadcast throughput. Simulation results show that the proposed algorithms can achieve high throughput under various network settings. [ABSTRACT FROM PUBLISHER]

Orthogonal frequency-division multiple access (OFDMA) has been integrated into emerging broadband wireless systems such as the IEEE 802.16 WirelessMAN. One critical problem in OFDMA is to assign multiple frequency bands (called subcarriers) to different users. Taking advantage of the frequency and multiuser diversities in OFDMA systems, dynamic subcarrier assignment mechanisms have been shown to achieve much higher downlink capacity than static assignment. A rich literature exists, which proposes medium-access-control- and physical-layer (MAC/PHY) schemes, aiming at exploiting the diversity gain with low implementation complexity. In this paper, we propose a cross-layer approach that explores the joint advantage of network coding and dynamic subcarrier assignment. With network coding, it becomes possible to assign the same subcarriers to different downlinks without causing interference. Consequently, our coding-aware assignment scheme improves the bandwidth efficiency and increases the downlink throughput by a substantial margin. In designing the scheme, we identify a tradeoff between diversity gain and the network coding advantage, which is critical to network performance in terms of throughput and fairness. To explore the tradeoff, we formulate the coding-aware assignment scheme as a mixed integer program and design a polynomial-time approximation algorithm that can be used in practical systems. We prove the asymptotic performance bound of the algorithm and demonstrate that it closely approximates the optimum under realistic experimental settings. [ABSTRACT FROM PUBLISHER]

In this paper, we examine the performance of uniform backoff (UB) and binary exponential backoff (BEB) algorithms with retry limit, which can be used in the random-access channels of Universal Mobile Telecommunication System (UMTS)-Long Term Evolution (LTE) and IEEE 802.16 systems under the assumption of finite population under unsaturated traffic conditions. Additionally, we consider access prioritization schemes to provide differential performance by controlling various system parameters. We show that controlling the persistence value as specified in UMTS is effective in both backoff algorithms. The performances with and without access prioritization schemes are presented in terms of throughput, mean, and variance of packet retransmission delay, packet-dropping probability, and system stability. Finally, we consider a dynamic window assignment algorithm that is based on Bayesian broadcasting, in which the base station adaptively controls the window size of the UB algorithm under unsaturated traffic conditions. Results show that the proposed window assignment algorithm outperforms fixed window assignment in static and dynamic traffic conditions under the assumption of perfect orthogonality between random-access codes. [ABSTRACT FROM PUBLISHER]