This paper studies an internet of things (IoT) network where a fusion center relies on multi-view and correlated information generated by multiple sources to monitor various regions. Each region possesses hard age of correlated information (AoCI) constraints for information update, and accordingly we propose a scheduling policy to satisfy such needs and minimize the required wireless resources. We first approximate the problem to a dual bin-packing problem. Secondly, efficient scheduling policies are identified when the age constraints possess special mathematical properties, where the number of channels at most required is analyzed. Optimality conditions of the proposed policies are presented. For general constraints, a two-step grouping algorithm for multi-view (TGAM) is proposed to establish scheduling policies. Under TGAM, the constraints are mapped into a combination of the special constraints. To quickly identify an optimized mapping from a vast solution space, TGAM heuristically groups the regions according to their constraints and then searches for the optimal mapping for each group. Numerical results demonstrate that, compared to a derived lower bound, the proposed TGAM requires only 1.07% more channels. Additionally, the number of regions that can be served by TGAM is significantly larger than the state-of-the art algorithm, given the number of channels.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, which is to determine a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing the economic and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need to (i) meet the travel delay requirements of customers and (ii) make online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. While the problem is combinatorial in nature and challenging, we exploit the two-stage structure to design an optimal solution that maximizes the expected reward of the route with polynomial time complexity, which makes it amenable to practical implementation. Our approach can be applied to optimize a variety of objectives in a ride-sharing trip, including revenue, driver’s profit, and total travel time reduction compared to serving customers individually. We carry out extensive simulations based on real-world travel demand traces in Manhattan. Simulation results show that applying our demand-aware solution increases the revenue by 26% against conceivable alternatives. As compared to the case without ride-sharing, our ride-sharing solution increases the driver’s profit per slot by 12% and decreases the total vehicle travel time (an indicator of greenhouse gas emission) by 16%.

In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale. In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes. However, larger batch sizes may also result in longer response time, and thus it requires a judicious design. This paper aims to provide a dynamic batching policy that strikes a balance between efficiency and latency. The GPU-based inference service is modeled as a batch service queue with batch-size dependent processing time. Then, the design of dynamic batching is a continuous-time average-cost problem, and is formulated as a semi-Markov decision process (SMDP) with the objective of minimizing the weighted sum of average response time and average power consumption. The optimal policy is acquired by solving an associated discretetime Markov decision process (MDP) problem with finite state approximation and “discretization”. By introducing an abstract cost to reflect the impact of “tail” states, the space complexity and the time complexity of the procedure can decrease by 63.5% and 98 %, respectively. Our results show that the optimal policies potentially possess a control limit structure. Numerical results also show that SMDP-based batching policies can adapt to different traffic intensities and outperform other benchmark policies. Furthermore, the proposed solution has notable flexibility in balancing power consumption and latency.

We consider collaborative perception (CP) systems where a fusion center monitors various regions by multiple sources. The center has different age of information (AoI) constraints for different regions. Multi-view sensing data for a region generated by sources can be fused by the center for a reliable representation of the region. To ensure accurate perception, differences between generation time of asynchronous status updates for CP fusion should not exceed a certain threshold. An algorithm named scheduling for CP with asynchronous status updates (SCPA) is proposed to minimize the number of required channels and subject to AoI constraints with asynchronous status updates. SCPA first identifies a set of sources that can satisfy the constraints with minimum updating rates. It then chooses scheduling intervals and offsets for the sources such that the number of required channels is optimized. According to numerical results, the number of channels required by SCPA can reach only 12% more than a derived lower bound.

In this work, we study a status update system with a source node sending timely information to the destination through a channel with random delay. We measure the timeliness of the information stored at the receiver via the Age of Information (AoI), the time elapsed since the freshest sample stored at the receiver is generated. The goal is to design a sampling strategy that minimizes the total cost of the expected time average AoI and sampling cost in the absence of transmission delay statistics. We reformulate the total cost minimization problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. Denote K to be the number of samples we have taken. We show that, when the transmission delay is bounded, the expected time average total cost obtained by the proposed online algorithm converges to the minimum cost when K goes to infinity, and the optimality gap decays with rate ${\mathcal{O}}$(ln K/K). Simulation results validate the performance of our proposed algorithm.

We consider a base station supporting two types of traffics, i.e., status update traffic and timely throughput traffic. The goal is to improve the information freshness of status update traffic while satisfying timely throughput constraints. Age of Information (AoI) is adopted as a metric for information freshness. We first propose an age-aware policy that makes scheduling decisions based on the current value of AoI directly. Given timely throughput constraint, an upper bound of the weighted average AoI under this policy is provided. To evaluate policy performance, it is important to obtain the minimum weighted average AoI achievable given timely throughput constraint. A low complexity method is proposed to estimate a lower bound of this value. Furthermore, inspired by the estimation procedure, we design an age-oblivious policy that does not rely on the current AoI to make scheduling decisions. Surprisingly, simulation results show that the weighted average AoI of the age-oblivious policy is comparable to that of the age-aware policy, and both are close to the lower bound.

We consider a star-topology wireless network for status update where a central node collects status data from a large number of distributed machine-type terminals that share a wireless medium. The Age of Information (AoI) minimization scheduling problem is formulated by the restless multi-armed bandit. A widely-proven near-optimal solution, i.e., the Whittle's index, is derived in closed-form and the corresponding indexability is established. The index is then generalized to incorporate stochastic, periodic packet arrivals and unreliable channels. Inspired by the index scheduling policies which achieve near-optimal AoI but require heavy signaling overhead, a contention-based random access scheme, namely Index-Prioritized Random Access (IPRA), is further proposed. Based on IPRA, terminals that are not urgent to update, indicated by their indices, are barred access to the wireless medium, thus improving the access timeliness. A computer-based simulation shows that IPRA's performance is close to the optimal AoI in this setting and outperforms standard random access schemes. Also, for applications with hard AoI deadlines, we provide reliable deadline guarantee analysis. Closed-form achievable AoI stationary distributions under Bernoulli packet arrivals are derived such that AoI deadline with high reliability can be ensured by calculating the maximum number of supportable terminals and allocating system resources proportionally.

Audio segmentation and classification is the basis of broadcast audio processing in the broadcast domain. A number of methods have been developed over the years in order for the broadcast station to segment and classify the audio into 7 classes, which are: male speech, female speech, speech with noise, speech with music, noise, music, and silence. Some of the major methods include SVM, GMM and HMM among others, however the overall accuracy of these existing methods is not satisfactory. This paper, therefore proposes an adaptive CNN method which classifies clips directly based on audio sample points. To our knowledge, this is the first work to do segmentation and classification in broadcast domain by Adaptive CNN. Each convolutional layer is equivalent to the clip feature extraction. The study uses the method of Adaptive CNN by increasing the amount of computation to ensure that the most suitable features can be extracted. In other words, Adaptive CNN increases the process of feature selection compared to CNN. A large number of experiments were carried out and compared with other approaches and the results show that the Adaptive CNN technique achieved a significant error reduction.

Audio segmentation and classification are the basis of audio processing in broadcasting industries. A Dual-CNN (Dual-Convolutional Neural Network) method is proposed in this article in which it is possible to pre-train a CNN with unlabeled audio data so as to deal with the scarcity of labeled data. Auto-encoders (including an encoder and a decoder) are utilized, thus the name “Dual.” In the first place, audio sampling points and the derived STFT (Short-Time Fourier Transform) spectrograms pass through their own CNNs. Fusion of the extracted features is then performed. Finally, the merged features are sent to a fully connected network and the classification results are produced via Softmax. Being one of the segmentation-by-classification approaches, our solution also presents a novel smoothing method (SEG-smoothing) in order to deliver the best result of segmentation. A series of experiments have been conducted and their result verifies that the proposed approach for segmentation and classification outperforms alternative solutions.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, in which the driver of a vehicle determines a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing economical and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need of (i) meeting travel delay requirements of customers, and (ii) making online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that the demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. We show that the problem is NP-Complete in the weak sense. We exploit the two-stage structure to design an optimal solution with pseudo-polynomial time complexity, which makes it amenable for practical implementation. We carry out extensive simulations based on real-world travel demand traces of Manhattan. The results show that using our demand-aware solution instead of the conventional greedy-routing scheme increases the driver's revenue by 10%. The results further show that as compared to the case without ride-sharing, our ride-sharing solution reduces the customers' payment by 9% and the total vehicle travel time (indicator of greenhouse gas emission) by 17%. The driver can also get 26% extra revenues per slot by participating in ride-sharing.