This paper investigates a hybrid service system with a cloud server and an in-house server. We consider two different scenarios: a hybrid service system with orbit space and a hybrid service system without orbit space. In the hybrid service system with orbit space, customers who fail to enter the cloud server can choose to join the in-house subsystem or to enter an orbit space and retry the cloud server. An admission control mechanism based on queue-length limitation is adopted to adjust whether the cloud service resources are open to customers. When the cloud server cannot be accessed immediately, some customers send their jobs to the in-house subsystem, while others (called opportunists) try to send their jobs to the cloud server again. We obtain the optimal queue-length limitation for a given retrial rate. The service provider and customers are different stakeholders, and their market forces are also different. Therefore, it is more realistic to explore the game relationship between them by using dynamic game theory. We can also explore the joint optimums of the queue-length limitation and the retrial rate in the framework of the Stackelberg game. Finally, by comparing with the hybrid service system without orbit space, we discuss the significance of the existence of orbit space, and gain management insights. It is found that the existence of opportunists may benefit the service provider, although they significantly harm social interests, regardless of whether they are cooperative or non-cooperative; therefore, opportunists are encouraged in some situations. Numerical analysis shows that adding a retrial orbit to a hybrid cloud service system with certain input parameters may even more than triple the service provider’s revenue.

Spectrum sensing is essential in opportunistic cognitive radio (CR) systems for detecting primary users’ (PUs’) activities and protecting the PUs against harmful interference. By extending the perfect spectrum sensing in the literature to more realistic situations, a new random sensing error framework of secondary users (SUs) is proposed to study the SUs’ behavior so as to make the best benefit for the CR system in this paper. A novel queueing-game theoretical model is formulated first and then various system stationary performance measures are procured. Furthermore, the SU’s equilibrium joining strategies are obtained, the throughput of SUs is derived, and the CR system’s social welfare’s monotonous in terms of the sensing error and the SU’s request frequency are characterized. Particularly, three interesting but counterintuitive results are observed as below: (i) the expected delay for joining SUs can be non-monotone in their effective arrival rate; (ii) multiple equilibrium joining strategies of SUs can always exist; and (iii) the spectrum sensing error does not necessarily worsen the CS system’s social welfare, i.e., some sensing error in the system may possibly lead to more efficient outcomes in terms of throughput and social welfare. The results and observations offered in this paper are expected to extend spectrum sensing research in a more efficient way to better provide CR system services to various users.

Cloud computing provides people with fast services, web service companies that need computing resources often submit some of their incoming jobs to the cloud and the rest to their in-house subsystem to seek services. This forms a hybrid service system with cloud service and an in-house server. Taking into account customers' strategic behavior, this article studies the optimal decision making problems in the hybrid service system. A queue-length-based admission control mechanism is adopted in this article to regulate whether the cloud server is open to customers. When the cloud server cannot be accessed immediately, some customers send their jobs to the in-house subsystem, while others (called opportunists) try to send their jobs to the cloud server again. We derive the conditional equilibrium joining probability of entering the in-house subsystem when the cloud is not open. Based on the cooperative and noncooperative game-theoretic analyses, we determine the cooperatively optimal retrial rate and the noncooperatively optimal retrial rate for a given queue-length information. Numerical experiments show that the two optimal retrial rates are equivalent. Finally, it is found that the existence of the opportunists significantly harms social interests, regardless of whether these opportunists are cooperative or noncooperative.

A vast body of literature usually has treated cognitive radio (CR) networks as single-band queues with preemptive priority for mathematical tractability, although, in practice, they are multiple-spectrum CR systems. In this article, we overcome these restrictions and employ a two-server preemptive priority queue to study CR systems with multiple spectrums. We obtain a joint optimal pricing strategy to maximize profit for the service provider (SP), as well as an optimal pricing strategy to maximize social welfare from the social administrator's viewpoint. We find that the socially optimal pricing strategy is to provide free service for all PUs, whereas the joint optimal pricing strategy for SP's maximum is equivalent to the pricing strategy that maximizes the two servers' own expected net benefit.

As mobile robots become more popular, more remote laboratories (RL) using mobile robots are being developed for online education. However, in most current RL for mobile robots, the control which users have over the robot is limited by the pre-programmed controlled algorithms. This limitation creates a gap in the learning experience of the students. This paper proposes an implementation of a fully programmable remote laboratory for mobile robots into a web application. In the RL user-interface, students are able to implement and execute their C++ source code and see the results in real-time through a live feed webcam. The AJAX technology is used to transfer data between the web application and web server, making it possible to execute the code. This application is designed to provide students with interactive tools and a contextual learning scene, making this research of significant relevance to online engineering education by providing students with direct programming experience on remote laboratory website.

A novel model is developed and investigated for a duty-cycle wireless sensor network (WSN), where each sensor node can stay at one of four statuses (i.e., full-active phase, two semi-active phases each with different functions, and a sleep phase) to make the system more efficient. The explicit result of the joint probability for data packet numbers and the sensor phases is achieved by suitably constructing a multi-dimensional Markov process. Furthermore, various energy consumption measures, such as energy consumption per unit time in each of the three energy consumptions and energy consumption switching from one phase to another, are obtained. System performance measures, such as the average delay time of a data packet in the system, throughput of the system, and the probability of the sensor node staying at any of the phases, are also achieved. The numerical analyses are provided to validate the model and the analytic results. The proposed model and the analysis method are expected to be applied to the design and analysis of WSN models with various phases of the sensor.

In a cognitive radio (CR) system, excessive access services for secondary users (SUs) lead to a substantial increase in congestion and the retrial phenomenon, both of which degrade the performance of CR networks, especially in overload conditions. This paper investigates the price-based spectrum access control policy that characterizes the network operator's provision to heterogeneous and delay-sensitive SUs through pricing strategies. Based on shared-use dynamic spectrum access (DSA), the SUs can occupy the dedicated spectrum without degrading the operations of primary users (PUs). The service to transmission of SUs can be interrupted by an arriving PU, while the interrupted SUs join a retrial pool called an orbit, later trying to use the spectrum to complete the service. In the retrial orbit, the interrupted SU competes fairly with other SUs in the orbit. Such a DSA mechanism is formulated as a retrial queue with service interruptions and general service times. Regarding the heterogeneity of delay-sensitive SUs, we consider two cases: the delay-sensitive parameter follows a discrete distribution and a continuous distribution, respectively. In equilibrium, we find that the revenue-optimal price is unique, while there may exist a continuum of equilibria for the socially optimal price. In addition, the socially optimal price is always not greater than the revenue-optimal price, and thus the socially optimal arrival rate is not less than the revenue-optimal one, which is contrary with the conclusion, i.e., the socially optimal and revenue-optimal arrival rates are consistent, drawn in the literature for homogeneous SUs. Finally, we present numerical examples to show the effect of various parameters on the operator's pricing strategies and SUs' behavior.

Massive MIMO is one of the key technologies in 5G wireless broadband, capable of delivering substantial improvements in capacity of next-generation wireless networks. However, due to its inherent complexity, its operation, reconfiguration, and enhancement present significant challenges and risks. In this paper we present RENEW, a fully programmable and observable massive MIMO network. We present the architectural design for full programmability at every layer of the wireless stack, from the radio hardware, including PHY and MAC layer configurations, all the way up to the network core functionality using network function virtualization. We also present mechanisms to enable observability at every layer of the stack. These include various indicators in the radio and core access network, hence enabling effective monitoring, troubleshooting, and performance evaluation of the network at large.

Wake-up radio (WuR) is a kind of ultra-low power transceiver that consumes energy at 1000 times lower in magnitude when compared to the main radio in traditional wireless sensors. When incorporated, traditional wireless sensor networks are possible to improve energy efficiency and packet delay simultaneously by mitigating idle listening and overhearing issues. In recent years, many works have designed and evaluated the performance of MAC protocols in WuR-enabled yet single-hop (i.e. star-shaped) wireless sensor networks. This paper moves to a multi-hop network and focuses on linear topology WuR-enabled WSNs. It makes practical sense as large-scale WSN topologies could be decomposed into multiple linear topologies. Based on WuR inherent characteristics and also signal interferences among adjacent sensors, we introduce some interesting design ideas and describe our proposed MAC protocol in detail. Analytical results on expected radio-on time of intermediate sensors when waken up are derived. Also numerical results based on normalized per-hop energy and delay ratios show the effectiveness of our protocol. It may serve as an interesting basis for potential researches into more realistically large-scale WuR-enabled WSNs.

Cognitive radio (CR) technology effectively overcomes spectrum inefficiency by providing the capability that unlicensed users can share the radio frequency spectrum with licensed users. In this paper, we consider a CR system with heterogeneous users. A single primary user (PU) randomly generates service requests and a licensed band processes these requests. This PU band also can be flexibly accessed by secondary users (SUs) if available. The CR system is regarded as a preemptive priority queueing system. Under different information levels, we investigate the equilibrium strategic behaviors of PU and SUs. Based on users' strategies, we study SU's sojourn time (i.e., the period beginning from the time an SU request enters the system and ending from the time the SU request is completed), and obtain the analytical solutions of SU's mean sojourn time. By theoretical and numerical analysis, the SU's mean sojourn time is found not decreasing with the service rate of PU. This phenomenon is counterintuitive, and it implies the increase of the service rate of PU does not necessarily reduce the mean sojourn time of SU. In this sense, we investigate and find optimal service rates of PU in different information levels to meet the PU's QoS requirement and simultaneously to maximize SU's throughput from the viewpoint of the service providers.

The equilibrium joining probabilities for a single primary user (PU) and secondary users (SUs) in the case of no queue length information, and the equilibrium Nash balance thresholds for PU and SUs to join the system in the cases of partial queue length information and full queue length information observed by PU and SUs are investigated in a cognitive radio system. PU stochastically sends PU requests and each SU is assumed to only carry one SU request. Whenever a PU (or an SU) request is completed, a reward is issued to the PU (or the SU). However, a holding cost is charged also for each SU and PU during their average sojourn time in the system respectively. Both of PU requests and SU requests are selfish in this research and their objectives are to maximize their own benefit. Our major conclusion includes a threshold probability value for PU (or SU) to join the system identified for the case of no queue length information, a Nash balance PU (SU) threshold value is determined for the case of partial queue length information; and the multi-dimensional Nash balance threshold is established for the case of full queue length information. These Nash balance threshold integer values depend on not only the Nash Balance PU-threshold value but also the specific number of SU requests in the system at that arriving time. In addition, we discuss the sensibility of input parameters to the obtained equilibrium joining probabilities and Nash balance thresholds. We mathematically verify that SUs’ equilibrium joining probability does not necessarily increase with the transmission rate of PU in no queue length information case and observe numerically that the Nash balance thresholds adopted by an arriving SU are fickle with the change of PU's transmission rate in both of partial and full queue length information cases. Our results and observations provide a guideline and important managerial insights in making decisions with strategic users in the design of CRNs.

Due to the rapid increases in the population of mobile social users, providing the users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource (like bandwidth, servers, computing power, etc.) in media cloud is limited, how to allocate resource among media cloud with brokers becomes a challenge. Media cloud can determine the price of the resource and a broker can decide whether it will pay the price for the resource when there is an incoming multimedia task (simplified as task). A broker can collect the revenues from the mobile social users by providing the multimedia services. Since resource is limited, the price will generally go up as the resource becomes more and more consumed. Therefore, in this paper, by assuming that accepting each task a broker can get a reward (by collecting revenues from mobile social users like online ads, etc.) and it needs pay some price (to the media cloud) for each task in the network, we concentrate on the optimization problems of when to admit or reject a task for a broker in order to achieve the maximum total discounted expected reward for any initial state. By establishing a discounted Continuous-Time Markov Decision Process (CTMDP) model, we verify that the optimal policies for admitting tasks are state-related control limit policies. Our numerical results with explanations in both tables and diagrams are consistent with our theoretic results.

This article investigates joining strategies and admission control policy for secondary users (SUs) with retrial behavior in a cognitive radio (CR) system where a single primary user (PU) coexists with multiple SUs. Under a certain reward-cost structure, SUs opportunistically access the PU band when it is not occupied by the PU. If the band is available upon arrival, an SU decides with a probability either to use the band immediately or to balk the system. If the band is occupied, the SU must decide whether to enter the system as a retrial customer or to leave the system. Once the PU requests for service, the service of SU being served, if any, will be interrupted, and the interrupted SU leaves the band and retries for service after a random amount of time. In this article, we study the equilibrium behavior of non-cooperative SUs who want to maximize their benefit in a selfish, distributed manner with delay-sensitive utility function. The socially optimal strategies of SUs are also derived. To utilize the PU band more efficiently and rationally, an admission control policy is proposed to regulate SUs who enter the system in order to eliminate the gap between the individually and socially optimal strategies.

The Internet of Things (IoT) is the internetworking of a variety of devices, including sensors. Among all sensors, visual sensors (i.e. cameras) are special because they can provide rich and versatile information. The world already has more than one billion cameras on mobile phones. We define the internetworking of visual sensors as the Internet of Video Things. This article estimates the number of cameras the world will see in 2030 and the implications of a large number of cameras. Transmitting, storing, and analyzing the data from cameras could impose significant challenges to existing technological infrastructures. This paper surveys recent progress in relevant technologies and suggests directions for future research.

The transmission times with generally random distributed for both primary user (PU) and secondary user (SU) in a cognitive radio system with a single PU band is investigated. To characterize the random access protocols of wireless networks, a retrial queueing system is employed to model the decentralized and centralized behavior of SUs. Based on the tradeoff between the service reward and the delay cost, each SU must decide whether to join or balk the system upon arrival based on different levels of information provided by the system. Two situations in which SUs have no information or partially observable information are studied. In each situation, two kinds of strategies are considered: 1) individual equilibrium strategies (i.e., non-cooperative strategies) that maximize SUs’ own profit and 2) socially optimal strategies (i.e., cooperative strategies) that maximize the expected social welfare. Moreover, comparisons are carried out between these two kinds of strategies. The results indicate that the equilibrium joining probability in the non-cooperative case is not less than the optimal joining probability in the cooperative case. To regulate the SUs’ behavior, an equilibrium pricing scheme is proposed to make these strategies coincide. In addition, we observe that the expected delay and the mean number of SUs in the system only depend on the first two moments of the SU’s transmission time. Numerical examples also show that the partially observable queue is more profitable from the perspective of the social planner.

This paper conducts the game-theoretic analysis of the behavior of secondary users (SUs) in a cognitive radio (CR) system with a single primary user (PU) band and sensing failures. It is assumed that the sensing errors occur only when an SU is being served by the PU band. It may incorrectly detect there is no PU accessing to the band (called misdetection), or it may wrongly sense that there is an incoming PU but in fact it is not true (called false alarm). When a misdetection occurs, the PU will be blocked and the ongoing SU will drop into a retrial pool called orbit in which it can retry for service after some random time. When a false alarm occurs, the ongoing SU will drop into the retrial orbit. That is, both errors will degrade the quality of service of the system. First, we investigate how the arriving SUs make decisions on whether to join or balk the system which can be studied as a non-cooperative game. We obtain the equilibrium behavior of SUs who want to maximize their benefit in a selfish distributed manner. Second, we derive the socially optimal strategies of SUs from the perspective of the social planner. To use the PU band more efficiently, an appropriate admission fee imposed on each joining SU is proposed based on the gap between the equilibrium strategy and the socially optimal strategy. Finally, theoretic results are validated by numerical analysis and the effect of various parameters on the behavior of SUs is illustrated.

We consider the strategic behavior of secondary users (SUs) in a cognitive radio system where SUs opportunistically share a single primary user (PU) band over a coverage area. The service of an SU can be interrupted by a PU in a preemptive manner, and the interrupted SU may abandon the system or wait until the PU band is sensed available. In the latter case, if spectrum sensing errors occur, they will cause misdetections and false alarms which impact the system’s performance heavily. In this paper, we model this problem as a retrial queueing system with server breakdowns and recoveries in which the interrupted SUs are treated as retrial customers. They will retry for using the PU band after some period of time due to interruptions or misdetections. The arrival of a PU during service of an SU is modeled as a server breakdown, and the recovery time is equivalent to the service time of this PU. We focus on the behavior of arriving SUs who can make decisions on whether to join the system or to balk based on a natural cost structure and the delays caused by PUs’ interruptions, which can be studied as a non-cooperative game. The equilibrium and optimal strategies of SUs are both derived. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed. Finally, some numerical examples are presented to show the effect of several key parameters on the system performance.

A cognitive radio (CR) system with retrial possibility and an admission cost for secondary users (SUs) to join the retrial group is investigated in this paper. If the SU finds the primary user (PU) band unavailable, it must decide with a probability estimate to either enter a retrial group or give up its service and leave the system. SUs in the retrial group independently retry after an exponentially distributed random time until they successfully access the spectrum. When the PU arrives, the SU's service on the band is interrupted. This interrupted SU is then assumed to occupy the PU band immediately when the PU completes its service. First, the noncooperative joining behavior of SUs that choose to maximize their benefit in a selfish distributed manner is investigated, and an inefficient Nash equilibrium is derived. Second, from the perspective of the social planner, the socially optimal joining strategy when SUs cooperate with each other is studied, and the corresponding Nash equilibrium is exactly derived. Finally, the result that an individually optimal strategy, in general, does not yield the socially optimal strategy is theoretically verified. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed and investigated with the derivation of an optimal value for the admission fee. The numerical analysis indicates that the proposed admission fee as an equilibrium strategy and the socially optimal strategy of SUs improve efficiency in the utilization of the CR system.

On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.

Wireless sensor networks (WSNs) have applications in many areas, such as biomedical observation, environmental monitoring, and battlefield surveillance. In order to reduce costs and improve reliability, target coverage has been an important problem for WSNs in recent years. In this paper, we investigate target coverage for a special kind of WSNs, called directional sensor networks, in which the sensors have limited monitoring distance and coverage angle. Given a set of targets and a set of sensors, we investigate how to assign proper directions for the sensors such that the number of covered targets is maximised. We show that it is NP-hard to find an optimal solution and we investigate both centralised and distributed approximation algorithms with provable performance guarantee. In addition, we analyse the problem itself from the combinatorics' point of view. Finally we also conduct a simulation experiment to evaluate the practical performance of proposed algorithms.