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The value maximization version of the secretary problem is the problem of hiring a candidate with the largest value from a randomly ordered sequence of candidates. In this work, we consider a setting where predictions of candidate values are provided in advance. We propose an algorithm that achieves a nearly optimal value if the predictions are accurate and results in a constant-factor competitive ratio otherwise. We also show that the worst-case competitive ratio of an algorithm cannot be higher than some constant <1/e , which is the best possible competitive ratio when we ignore predictions, if the algorithm performs nearly optimally when the predictions are accurate. Additionally, for the multiple-choice secretary problem, we propose an algorithm with a similar theoretical guarantee. We empirically illustrate that if the predictions are accurate, the proposed algorithms perform well; meanwhile, if the predictions are inaccurate, performance is comparable to existing algorithms that do not use predictions. [ABSTRACT FROM AUTHOR]

The broadcast nature of communication in transmission media has driven the rise of network coding’s popularity in wireless networks. Numerous benefits arise from employing network coding in multi-hop wireless networks, including enhanced throughput, reduced energy consumption, and decreased end-to-end delay. These advantages are a direct outcome of the minimized transmission count. This paper introduces a comprehensive framework to employ network coding in these networks. It refines decision-making at coding and decoding nodes simultaneously. The coding-nodes employ optimal stopping theory to find optimal moments for packet transmission. Meanwhile, the decoding-nodes dynamically decide, through SMDP (Semi Markov Decision Process) problem formulation, whether to conserve energy by deactivating radio units or to stay active for improved coding by overhearing packets. The proposed framework, named ENCODE, enables nodes to learn how and when to use network coding over time. Simulation results compare its performance with existing approaches. Our simulation results shed new light on when and how to use network coding in wireless multi-hop networks more effectively.

Mobile edge computing (MEC) paradigm has emerged to improve the quality of service & experience of applications deployed in close proximity to end-users. Due to their restricted computational and communication resources, MEC nodes can provide access to a portion of the entire set of services and data gathered. Therefore, there are several obstacles to their management. Keeping track of all the services offered by the MEC nodes is challenging, particularly if their demand rates change over time. Received tasks (such as, analytics queries, classification tasks, and model learning) require services to be invoked in real MEC use-case scenarios, e.g., smart cities. It is not unusual for a node to lack the necessary services or part of them. Undeniably, not all the requested services may be locally available; thus, MEC nodes must deal with the timely and appropriate choice of whether to carry out a service replication (pull action) or tasks offloading (push action) to peer nodes in a MEC environment. In this study, we contribute with a novel time-optimized mechanism based on the optimal stopping theory, which is built on the cost-based decreasing service demand rates evidenced in various service management situations. Our mechanism tries to optimally solve the decision-making dilemma between pull and push action. The experimental findings of our mechanism and its comparative assessment with other methods found in the literature showcase the achieved optimal decisions with respect to certain cost-based objective functions over dynamic service demand rates.

To address the low spectrum utilization issue of the distributed network due to multi-user collision and channel time-varying nature, the distributed channel access problem for multicast communication was investigated.Based on the optimal stopping theory, a statistical model of distributed channel access under wireless multicasts was established, and an optimal distributed wireless channel access method under reliable multicast communication was proposed.Each source competes for the channel in a distributed manner, the winner source determines whether to access the shared channel by comparing the reliable multicast access rate with a pure threshold to complete the reliable multicast communication from the winner source to all the sinks.Theoretic optimality of the method was proved rigorously.A corresponding low-complexity algorithm was designed, which has a pure threshold structure and good engineering practicability.Numerical results show that the proposed channel access method can effectively improve the average throughput of the system.

As the number of satellites increases, the risk of fixing wrong integer ambiguities may reduce the accuracy and efficiency of ambiguity resolution (AR); Thus, partial ambiguity resolution (PAR) is proposed for solving full AR (FAR) based on fixing the subset of ambiguities. Batch PAR with integer least-squares (ILS) is helpful, but selecting an optimal subset must compare all possible combinations, which is time-consuming. In this study, we introduced the classic optimal stopping theory (OST) to dynamically identify ambiguity subsets in batch PAR, which aims to maintain the accuracy and reduce the process time. The process of selecting the ambiguity subset was divided into two stages: observation and decision. All the options in the observation stage were discarded, and the choice was only made in the decision stage. The classic OST has demonstrated that there is a maximum 37% probability of selecting an optimal option when the range of observations is 37% based on six assumptions. To better explain the principle of OST in subset selection and provide a reference method in batch PAR, we chose the ambiguity dilution of precision (ADOP) PAR as the subject and put forward the OST-assisted ADOP PAR (OAPAR) to obtain the fixed solutions. The static relative positioning experiment began with reproducing a 37% observation range in OST (OST-37%) using the global positioning system (GPS) L1 static observation. The 37% observation range has a maximum probability of 41% in selecting the subset with the minimum ADOP and has less consumed time compared to other larger observation ranges; Then, FAR and minimum ADOP PAR (MAPAR) models are used to verify the accuracy and efficiency of OAPAR. The static results of different system combinations show that, compared to MAPAR, OAPAR could achieve the same accuracy while saving consumed time, especially when the number of ambiguities is higher. Finally, PAR based on success rate (SRPAR) is used to analyze the advantages and disadvantages of OAPAR. Although the probability of correct fixing of OAPAR is slightly smaller than SRPAR, the convergence time of OAPAR is better than SRPAR. Meanwhile, with the number of ambiguities increase, the RMSEs of OAPAR gradually perform smaller than SRPAR. Furthermore, the classic OST proposes a solution to the extreme value problem in global navigation satellite system (GNSS) positioning and navigation. [ABSTRACT FROM AUTHOR]

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

In hierarchical federated learning,energy constrained mobile devices will consume their own resources for participating in model training.In order to reduce the energy consumption of mobile devices,this paper proposes the problem of minimizing the sum of energy consumption of mobile devices without exceeding the maximum tolerance time of hierarchical federated learning.Different training rounds of edge server can select different mobile devices,and mobile devices can also train models under diffe-rent edge servers concurrently.Therefore,this paper proposes ODAM-DS algorithm based on an online double auction mechanism.Based on the optimal stopping theory,the edge server is supported to select the mobile device at the best time,so as to minimize the average energy consumption of the mobile device.Then,the theoretical analysis of the proposed online double auction mechanism proves that it meets the characteristics of incentive compatibility,individual rationality and weak budget equilibrium constraints.Simulation results show that the energy consumption of ODAM-DS algorithm is 19.04% lower than that of the existing HFEL algorithm.

Device-edge co-inference opens up new possibilities for resource-constrained wireless devices (WDs) to execute deep neural network (DNN)-based applications with heavy computation workloads. In particular, the WD executes the first few layers of the DNN and sends the intermediate features to the edge server that processes the remaining layers of the DNN. By adapting the model splitting decision, there exists a tradeoff between local computation cost and communication overhead. In practice, the DNN model is re-trained and updated periodically at the edge server. Once the DNN parameters are regenerated, part of the updated model must be placed at the WD to facilitate on-device inference. In this paper, we study the joint optimization of the model placement and online model splitting decisions to minimize the energy-and-time cost of device-edge co-inference in presence of wireless channel fading. The problem is challenging because the model placement and model splitting decisions are strongly coupled, while involving two different time scales. We first tackle online model splitting by formulating an optimal stopping problem, where the finite horizon of the problem is determined by the model placement decision. In addition to deriving the optimal model splitting rule based on backward induction, we further investigate a simple one-stage look-ahead rule, for which we are able to obtain analytical expressions of the model splitting decision. The analysis is useful for us to efficiently optimize the model placement decision in a larger time scale. In particular, we obtain a closed-form model placement solution for the fully-connected multilayer perceptron with equal neurons. Simulation results validate the superior performance of the joint optimal model placement and splitting with various DNN structures. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and solve a class of optimal stopping problems of recursive type. In particular, the stopping payoff depends directly on the value function of the problem itself. In a multidimensional Markovian setting, we show that the problem is well posed in the sense that the value is indeed the unique solution to a fixed point problem in a suitable space of continuous functions, and an optimal stopping time exists. We then apply our class of problems to a model for stock trading in two different market venues, and we determine the optimal stopping rule in that case. [ABSTRACT FROM AUTHOR]

Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC. [ABSTRACT FROM AUTHOR]

Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks. [ABSTRACT FROM AUTHOR]

This paper addresses the risk of fraud in credit card transactions by developing a probabilistic model for the quickest detection of illegitimate purchases. Using optimal stopping theory, the goal is to determine the moment, known as disorder or fraud time, at which the continuously monitored process of a consumer's transactions exhibits a disorder due to fraud, in order to return the best trade-off between two sources of cost: on the one hand, the disorder time should be detected as soon as possible to counteract illegal activities and minimize the loss that banks, merchants and consumers suffer; on the other hand, the frequency of false alarms should be minimized to avoid generating adverse effects for cardholders and to limit the operational and process costs for the card issuers. The proposed approach allows us to score consumers' transactions and to determine, in a rigorous, personalized and optimal manner, the threshold with which scores are compared to establish whether a purchase is fraudulent. [ABSTRACT FROM AUTHOR]

The High Efficiency Video Coding (HEVC) efficiently reduces the size of the multimedia contents, but at the cost of high computation complexity. In order to make it work for the real time applications, a study is conducted in this article to speed up the intra mode decision of HEVC. Firstly, a novel early termination mechanism is proposed that is based on the duration problem. This model is designed such that it requires the least amount of computation and dynamically adjusts to the current best option. Secondly, a dynamic threshold is proposed that outperformed majority of the thresholds in-terms of computation and adjustment with-respect-to the current best option. Experimental results proved that when the proposed methods are incorporated in HEVC, they reduced the encoding time by 34.1% on average, with a minor bit-rate (BD-BR) increase of 1.4% on average. [ABSTRACT FROM AUTHOR]

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to include new messages of varying importance, control, and monitoring. To efficiently and effectively control the exchange of such messages subject to the stochastic nature of the underlying wireless network, we design a time-optimized, dynamic, and distributed decisionmaking mechanism based on the principles of the Optimal Stopping and Change Detection theories. The findings from our experimentation platform are promising and solidly supportive to a vast spectrum of realtime and latency-sensitive applications with quality-of-service requirements in mobile IoT environments. [ABSTRACT FROM AUTHOR]

In order to support fast distribution of multimedia contents on social media and internet, a fast intra mode decision strategy is proposed in this article to reduce the encoding time of multimedia contents by removing the brute force intra mode selection procedure of HEVC. This article, firstly, improves the performance of the rough-mode-decision (RMD) component of HEVC by constructing the candidate intra mode list by employing a new measure that is fusion of the Hadamard-cost and the statistical-inference formed using spatial/ temporal information. Later, an optimal stopping point prediction model is constructed that outperforms the existing optimal stopping models proposed for HEVC by giving promising balance between the increase in bit-rate and the decrease in complexity. Finally, an early intra mode termination is predicted using the proposed optimal stopping model. Simulation results demonstrate that the proposed model has a wide application and provides early termination for a generic class of decision problems. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless network technology, energy saving has become a very important topic to build a green network in wireless networks. Due to the time-varying characteristics of the channel, it is possible to obtain a higher utilization for energy by using the channel with good state in wireless communications. From the view of the data transmission energy consumption of the whole wireless network, this paper proposes an expected energy consumption minimization strategy (E2CMS) for data transmission based on the optimal stopping theory. Considering the maximum transmission delay and a given receiving power, E2CMS delays data to transmit until the best expected channel state is detected. In order to solve the problem, firstly, we construct an energy consumption minimization problem with quality of service constraints. Then, we prove that E2CMS is a pure threshold strategy by the optimal stopping theory and obtain the power threshold by solving a fixed-point equation using backward induction. Finally, simulations are performed in a typical small-scale fading channel model. E2CMS is compared with a variety of different transmission scheduling strategies. The results show that E2CMS has lower average energy consumption for per unit data and significantly improves the network performance. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things (IoT) offers a vast infrastructure of numerous interconnected devices capable of communicating and exchanging data. Pervasive computing applications can be formulated on top of the IoT involving nodes that can interact with their environment and perform various processing tasks. Any task is part of intelligent services executed in nodes or the back end infrastructure for supporting end users' applications. In this setting, one can identify the need for applying updates in the software/firmware of the autonomous nodes. Updates are extensions or patches significant for the efficient functioning of nodes. Legacy methodologies deal with centralized approaches where complex protocols are adopted to support the distribution of the updates in the entire network. In this paper, we depart from the relevant literature and propose a distributed model where each node is responsible to, independently, initiate and conclude the update process. Nodes monitor a set of metrics related to their load and the performance of the network and through a time-optimized scheme identify the appropriate time to conclude the update process. We report on an infinite horizon optimal stopping model on top of the collected performance data. The aim is to make nodes capable of identifying when their performance and the performance of the network are of high quality to efficiently conclude the update process. We provide specific formulations and the analysis of the problem while extensive simulations and a comparison assessment reveal the advantages of the proposed solution. Highlights • We propose an time optimized, performance-aware scheme for software updates management. • The mechanism decides when an update process should be activated. • We offer a method for deriving the optimal time for initiating the update process. • With the proposed mechanism, IoT nodes enjoy the best possible performance. • We offer extensive simulations and an analysis of the results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a broadcast dissemination protocol for messages in opportunistic networks (OppNet) that is efficient in terms of energy consumption and network capacity usage, while not increasing the number of excluded nodes (nodes not receiving messages). The majority of the OppNet broadcast delivery schemes proposed in the literature, do not take into consideration that reducing energy and buffer usage is of paramount importance in these wireless networks normally consisting of small devices. In our protocol, broadcast messages are limited by carefully selecting their prospective forwarders (storers). The keystone of our protocol is the use of Optimal Stopping Theory, which selects the best message storers at every stage of the algorithm, while holding back broad message dissemination until convenient conditions are met. The broadcast efficiency of the proposed protocol out competes other OppNet broadcast proposals in four well-known scenarios. Furthermore, the protocol reduces the number of both dropped messages and nodes not receiving messages, thus maximising network capacity usage, and the span of the message delivery. [ABSTRACT FROM AUTHOR]

Cognitive cooperative relaying communication significantly improves the performance of wireless communication networks and spectral efficiency.Optimal relay selection problem,which characterize the desired tradeoff between the probing cost for establishing cooperative transmission and hence higher reward via cooperative diversity,is a hard issue when the number of relay nodes is large.Optimal relay selection strategy was established by using optimal stopping theory with a finite horizon.Furthermore,a pure SNR（signal-to-noise ratio）threshold structure was exhibited.Theoretical analysis and numerical results indicate that proposed optimal relay selection strategy is effective in reducing the number of probing candidate relays and improving the reward of source.

Aiming at the performance optimization problem of the virtual machine migration process in mobile cloud computing, this paper proposed a pre-copy transmission strategy based on the optimal stopping theory. The strategy solved the optimal stopping model which looked for the optimal transmission rate with the optimal stopping theory,to obtain the optimal transmission rate. So that it resulted in the reduction of the total amount of migration data and the total time in the virtual machine migration process. In the simulation experiment .this paper compared the proposed transmission strategy with the referenced transmission strategies, and analyzed the performance of different transmission strategies. The experimental results show' that the proposed strategy get less total amount of migration data and total time,and can effectively improve the performance of the migration process. [ABSTRACT FROM AUTHOR]

This paper compares two novel methods applied to Portfolio Management based on the attractive theory of Optimal Stopping Problems. We test the single criterion standard version of the latter theory against the multi-criteria version. The optimal moment to stop and trade (to Buy or Sell), represents the major challenge of our active management strategy. We subject the stock included in the portfolio to the rules derived from the underlying theory. Our aim is to provide a method that helps portfolio managers create wealth by buying and selling securities (trading). Our algorithm proves its performance when applied to real data, and we compare it with the Buy & Hold Strategy. [ABSTRACT FROM AUTHOR]

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.

The vision of Internet of Things (IoT) aims to offer a vast infrastructure of numerous interconnected devices usually called IoT nodes. The infrastructure consists of the basis of pervasive computing applications. Applications can be built with the participation of the IoT nodes that interact in very dynamic environments. In this setting, one can identify the need for applying updates in the software/firmware of the autonomous nodes. Updates may include software extensions and patches significant for the efficient functioning of the IoT nodes. Legacy methodologies involve centralized models where complex algorithms and protocols are adopted for the distribution of the updates to the nodes. This paper proposes a distributed approach where each node is responsible to initiate and conclude the update process. We envision that each node monitors specific performance metrics (related to the node itself and/or the network) and based on a time-optimized scheme identifies the appropriate time to perform the update process.We propose the adoption of a finite horizon optimal stopping scheme . Our stopping model originates in the Optimal Stopping Theory (OST) and takes into account multiple performance metrics. The aim is to have the nodes capable of identifying when their performance and the performance of the network are of high quality. In that time, nodes could be able to efficiently conclude the update process. We provide a set of formulations and the analysis of our problem. Extensive experiments and a comparison assessment reveal the advantages of the proposed solution. [ABSTRACT FROM AUTHOR]

Network coding is all about combining a variety of packets and forwarding as much packets as possible in each transmission operation. The network coding technique improves the throughput efficiency of multi-hop wireless networks by taking advantage of the broadcast nature of wireless channels. However, there are some scenarios where the coding cannot be exploited due to the stochastic nature of the packet arrival process in the network. In these cases, the coding node faces 2 critical choices: forwarding the packet towards the destination without coding, thereby sacrificing the advantage of network coding, or waiting for a while until a coding opportunity arises for the packets. Current research works have addressed this challenge for the case of a simple and restricted scheme called reverse carpooling where it is assumed that 2 flows with opposite directions arrive at the coding node. In this paper, the issue is explored in a general sense based on the COPE architecture requiring no assumption about flows in multi-hop wireless networks. In particular, we address this sequential decision making problem by using the solid framework of optimal stopping theory and derive the optimal stopping rule for the coding node to choose the optimal action to take, ie, to wait for more coding opportunity or to stop immediately (and send packet). Our simulation results validate the effectiveness of the derived optimal stopping rule and show that the proposed scheme outperforms existing methods in terms of network throughput and energy consumption. [ABSTRACT FROM AUTHOR]

This work focuses on the incorporation of incentive stimulations into data dissemination in autonomous mobile social networks with selfish nodes. The key challenge of enabling incentives is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest data types. We propose two data dissemination models: the data pulling model where mobile users pull data from data providers, and the data pushing model where data providers generate personalized data and push them to the intended users. For data pulling, we present effective mechanisms to estimate the expected credit reward of a message that helps intermediate nodes to evaluate the potential reward of it. Nodal message communication is formulated as a two-person cooperative game, whose solution is found by a heuristic approach which achieves Pareto optimality. Under the data pushing model, “virtual checks” are introduced to eliminate the needs of accurate knowledge about whom and how many credits data providers should pay. The check buying process is formulated as an online auction model to further accelerate the circulation of credits. Extensive simulations carried out based on real-world traces show the proposed schemes achieve better performance than fully cooperative scheme, but significantly reduce communication cost. [ABSTRACT FROM AUTHOR]

Delivery delay and communication costs are two conflicting design issues for mobile opportunistic networks with nonreplenishable energy resources. In this paper, we study the optimal data dissemination for resource constrained mobile opportunistic networks, i.e., the delay-constrained least-cost multicasting in mobile opportunistic networks. We formally formulate the problem and introduce a centralized heuristic algorithm which aims to discover a tree for multicasting, in order to meet the delay constraint and achieve low communication cost. While the above algorithm can be implemented by each individual node, it is intrinsically centralized (requiring global information) and, thus, impractical for real-world implementation. However, it offers useful insights for the development of a distributed scheme. The essence of the centralized approach is to first learn the probabilities to deliver the data along different paths to different nodes and then decide the optimal multicast tree by striking the balance between cost and delivery probability. In mobile opportunistic networks, even if the optimal routing tree can be computed by the centralized solution, it is the “best” only on a statistic basis for a large number of data packets. It is not necessarily the best solution for every individual transmission. Based on the above observation, we develop a distributed online algorithm using optimal stopping theory, in which in each meeting event, nodes make adaptive online decisions on whether this communication opportunity should be exploited to deliver data packets. We carry out simulations to evaluate the scalability of the proposed schemes. Furthermore, we prototype the proposed distributed online multicast algorithm using Nexus tablets and conduct an experiment that involves 37 volunteers and lasts for 21 days to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g., interactive data subspace explorations, data subspace visualizations, and in query processing optimization. However, in many modern data systems, predictive analytics may be (i) too costly money-wise, e.g., in clouds, (ii) unreliable, e.g., in modern Big Data query engines, where accurate statistics are difficult to obtain/maintain, or (iii) infeasible, e.g., for privacy issues. We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in terms of prediction and accommodating the well-known selection queries: multi-dimensional range and distance-nearest neighbors (radius) queries. Our function estimation model: (i) quantizes the vectorial query space, by learning the analysts' access patterns over a data space, (ii) associates query vectors with their corresponding cardinalities of the analyst-defined data subspaces, (iii) abstracts and employs query vectorial similarity to predict the cardinality of an unseen/unexplored data subspace, and (iv) identifies and adapts to possible changes of the query subspaces based on the theory of optimal stopping. The proposed model is decentralized, facilitating the scaling-out of such predictive analytics queries. The research significance of the model lies in that (i) it is an attractive solution when data-driven statistical techniques are undesirable or infeasible, (ii) it offers a scale-out, decentralized training solution, (iii) it is applicable to different selection query types, and (iv) it offers a performance that is superior to that of data-driven approaches. [ABSTRACT FROM AUTHOR]

We study zero-sum optimal stopping games (Dynkin games) between two players who disagree about the underlying model. In a Markovian setting, a verification result is established showing that if a pair of functions can be found that satisfies some natural conditions then a Nash equilibrium of stopping times is obtained, with the given functions as the corresponding value functions. In general, however, there is no uniqueness of Nash equilibria, and different equilibria give rise to different value functions. As an example, we provide a thorough study of the game version of the American call option under heterogeneous beliefs. Finally, we also study equilibria in randomized stopping times. [ABSTRACT FROM AUTHOR]

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Optimal stopping theory is a key part of sequential analysis and applied probability. Dr. Sören Christensen’s article is an important contribution to solving a class of optimal stopping problems for Markov processes mainly in continuous time, but a certain expansion to discrete time is also given. This discussion contains several issues that naturally arise in many statistical applications. [ABSTRACT FROM PUBLISHER]

On-line statistical and machine learning analytic tasks over large-scale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with quality-unaware methods over real sensory multivariate contextual data. [ABSTRACT FROM AUTHOR]

Opportunistic scheduling for a wireless network with hybrid links is studied in this paper. Specifically, two link types are considered: a link of the first type always has a much lower transmission rate than a link of the second type. To avoid starvation in the first type of links, two link types must be treated differently in opportunistic scheduling, and quality of service (QoS) constraints, such as maximum delay or minimum throughput, must be imposed on the first link type. Considering QoS constraints, a distributed opportunistic scheduling scheme is derived based on the optimal stopping theory. Two scenarios are considered for the QoS-oriented opportunistic scheduling scheme. In the first scenario, all links within the same link type follow the same rate distribution. Thus, QoS constraints are imposed on the entire link type. In the second scenario, links of the first type follow heterogeneous rate distributions. Thus, QoS requirements need to be imposed on links with the worst performance. Performance results show that the new opportunistic scheduling scheme outperforms the existing ones in most scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we propose effective data collection schemes that stimulate cooperation between selfish users in mobile opportunistic crowdsensing. A query issuer generates a query and requests replies within a given delay budget. When a data provider receives the query for the first time from an intermediate user, the former replies to it and authorizes the latter as the owner of the reply. Different data providers can reply to the same query. When a user that owns a reply meets the query issuer that generates the query, it requests the query issuer to pay credits. The query issuer pays credits and provides feedback to the data provider, which gives the reply. When a user that carries a feedback meets the data provider, the data provider pays credits to the user in order to adjust its claimed expertise. Queries, replies and feedbacks can be traded between mobile users. We propose an effective mechanism to define rewards for queries, replies and feedbacks. We formulate the bargain process as a two-person cooperative game, whose solution is found by using the Nash theorem. To improve the credit circulation, we design an online auction process, in which the wealthy user can buy replies and feedbacks from the starving one using credits. We have carried out extensive simulations based on real-world traces to evaluate the proposed schemes. [ABSTRACT FROM AUTHOR]

The opening of the ultrahigh frequency (UHF) TV band by the Federal Communications Commission for opportunistic operation promises to relieve the demand on the industrial, scientific, and medical (ISM) bands. However, supporting bandwidth-intensive applications over TV white spaces can be quite challenging, due to the unpredictable nature of spectrum availability and the fluctuations in channel quality. The realization of this Herculean feat through unlicensed usage, while providing protection to licensed primary users, requires intelligent and adaptive protocol design. In this paper, we propose a quality-of-service-aware parallel sensing/probing architecture (QASPA), which exploits inherent channel and user diversity exhibited by the wireless system. Aiming at maximizing the sensing efficiency while achieving high detection accuracy, QASPA incorporates an optimal adaptive double-threshold-based sensing mechanism. It also embodies a cross-layer protocol, which uses an adaptive framing structure to minimize the control overhead, as well as a novel spectrum assignment strategy targeted at improving the spatial reuse of the network. The proposed spectrum assignment strategy supports both channel bonding and aggregation. Our simulations validate the ability of QASPA in guaranteeing the demands of high-bandwidth opportunistic flows while supporting low-bandwidth flows. They also show the superior performance of QASPA compared with the scheme used in the ECMA-392 standard for opportunistic indoor streaming. [ABSTRACT FROM AUTHOR]