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Exploiting the sparse user activity induced by sporadic transmission, compressed sensing (CS) has been widely applied in multiple-input multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) for efficient multiuser detection. However, most of the existing detection schemes in MIMO enabled NOMA systems focus mainly on the spatial structure in user activity induced by multiantenna reception, while the temporal correlation in user activity has rarely been incorporated for further performance improvement. To address this issue, we propose a novel Multiuser Detection framework in MIMO enabled NOMA (MDMN), which explicitly integrates the temporal correlation in user activity into the detection process to improve the user detection accuracy. Specifically, we first formulate multiuser detection with multiantenna reception in MDMN as a block CS problem, by exploiting the spatial correlation in user activity. Furthermore, through incorporating the temporal correlation in active user sets into block CS, an adaptive CS algorithm based on subspace pursuit is developed for MDMN, named spatial-temporal correlation enhanced adaptive subspace pursuit (STASP). In particular, STASP does not require any prior knowledge of the user sparsity, as the cross validation designed in STASP can properly terminate the algorithm. The superior performance of the proposed MDMN framework and the corresponding algorithms is corroborated by extensive simulation results. [ABSTRACT FROM AUTHOR]

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality. [ABSTRACT FROM AUTHOR]

It is important to transmit data reliably, and efficiently in vehicular networks. Existing works usually study routing strategies, and cooperative scheduling to improve the efficiency of transmission. However, the data transmission remains inefficient because of the lack of full use of communication resources. The transmission is unreliable because information cannot be completely transmitted to the destination vehicles. Moreover, the increasing number of connected vehicles, and the limitation of available communication resources make task scheduling challenging in vehicular networks. In this work, we propose Cluster-enabled Cooperative Scheduling based on Reinforcement Learning (CCSRL) to improve the communication efficiency, and reliability of vehicular networks, with the goal of maximizing the information capacity. In particular, we leverage the stability to select a cluster head vehicle to enhance data transmission efficiency, and a reinforcement learning-based auxiliary transmission is further designed to guarantee the reliable communication among vehicles. The experimental results demonstrate that the performance of the proposed scheduling algorithm, especially the performance of the packet delivery ratio, and node packet loss ratio, is better than that of the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Recently radio frequency identification (RFID) is promoting the rapid development of Internet of Things and has been widely applied in numerous industrial applications such as inventory management, object tracking and smart logistics, etc. Tags in some RFID applications can be classified into multiple categories according to the types of objects they are attached to. One of the important functionalities for the multi-category RFID systems is the missing tag cardinality estimation of each category. This paper focuses on solving the missing tag iceberg query problem for multi-category RFID systems, i.e., to determine a set of categories, whose missing tags are more than a threshold, with a required reliability. We firstly propose two basic missing tag i ceberg query schemes called MAC-SZE and MAC-HZE, which adopt the singleton-zero estimator and homogeneous-zero estimator, respectively. To further improve the query efficiency, we then propose two segmented enhanced schemes called MAC-SSZE and MAC-SHZE, which eliminate the unnecessary slots in each frame. We conduct theoretical analysis to guarantee the required reliability is satisfied. The simulations are finally conducted and the results illustrate that the proposed missing tag iceberg query schemes greatly outperform other existing one. [ABSTRACT FROM AUTHOR]

In many radio-frequency identification (RFID) applications, one of the essential systematic functionalities is to quickly detect missing-tag event in case of misplacement or other incorrect operations. In this paper, we focus on probabilistically detecting the missing tags for the anonymous multicategory RFID systems without revealing the tag privacy. The main objective is to minimize the detection time while satisfying the required detection reliability of each category. First, we propose to use a multihash technique to sequentially detect the missing tags category-by-category, called segmented sequential detection approach, in which the frame segmentation is adopted to reduce the detection time. Then, we propose an enhanced segmented sequential detection approach to further improve the detection efficiency by deactivating the identified existing tags. We conduct extensive simulations to illustrate the effectiveness of our proposed two missing tag detection approaches. [ABSTRACT FROM AUTHOR]

Delay-tolerant networks (DTNs) are an emergent communication paradigm characterized by intermittent connectivity. The nodes in DTNs can take advantage of their contact opportunities to forward messages. However, in noncooperative DTNs, the nodes may be selfish and reluctant to cooperate with each other in message forwarding. In such DTNs, stimulating cooperation among the nodes will be indispensable. Recently, many incentive mechanisms have been proposed to motivate nodes to cooperate in message forwarding. However, most of them cannot guarantee systematic security. To resolve the drawback of the previous incentive mechanisms, we first propose a credit-based rewarding scheme called the earliest path singular rewarding (EPSR) scheme to motivate the nodes to truthfully forward the messages during every contact opportunity. Then, we propose another credit-based rewarding scheme called the earliest path cumulative rewarding (EPCR) scheme by further considering that a node may get more contact information on others. We prove that both the EPSR and EPCR schemes are incentive compatible, and the payment for each delivered message is upper bounded. Furthermore, the proposed schemes can prevent selfish nodes having malicious behaviors. We have conducted real-trace-based simulations to illustrate the effectiveness of the proposed EPSR and EPCR schemes. [ABSTRACT FROM AUTHOR]

With the promising technology of far-field wireless power transfer, wearable devices are able to harvest radio frequency (RF) energy from RF-based chargers and thus operate continuously. Rational planning of the quantity and positions of the chargers provide an effective way to improve the recharging efficiency and save the charger deployment budget. Existing work on RF-based charger placement mainly considers the situation where the devices are static, or adopt very simple mobility model. In this paper, we consider wireless charging service provision for wearable devices worn by users in a two-dimensional area, with users having a specific stay–move behavior pattern characterized by the trajectories, stay points, and stay time distribution. Based on this mobility pattern, we formulate the problem as how to find a charger placement to minimize the charging service budget, subject to the power non-outage probability requirement of the wearable devices. We further transform this problem to several equivalent problems for easier tractability and prove that it is NP-complete. Both greedy heuristic and particle swarm optimization (PSO) based solutions are proposed to solve the problem with certain approximation ratio. Finally, performances of the proposed solutions are compared with exhaustive search and existing point provisioning algorithm through extensive simulations. Simulation results show that both greedy heuristic and PSO-based solutions outperform the baseline algorithms, while PSO-based solution requires least number of chargers and thus is the most cost-effective. [ABSTRACT FROM AUTHOR]