The stack loading problem has been studied in recent years for its great impact on the container loading and unloading operations. Among different objectives of the problem considered, minimizing the total number of unordered stackings and minimizing the total number of used stacks are the two important ones, which ensure efficient loading and unloading schedules, as well as reduce storage costs, respectively. The load-bearing setting, where each container has its own weight and bearing weight, is frequently considered in box packing operations but rarely in the existing studies on the stack loading problem. However, the load-bearing constraint on containers is very important for stack loading, because safety is of paramount importance. This paper is the first study on the stack loading problem with the load-bearing constraint with an aim to minimize the number of stacks and the number of unordered stackings. We show that this problem is strongly$\mathcal{NP}$-hard even when the number of stacks is given and equals$2$. For the case where the number of stacks is given and jobs on the bottom tiers are fixed, we show that the problem can be solved by dynamic programming in pseudo-polynomial time. For the general problem, based on a two-index integer linear programming formulation and a tabu search heuristic, we develop a binary-search based matheuristic. Our experimental results demonstrate the efficiency and effectiveness of the newly developed matheuristic.Note to Practitioners—This paper is motivated by the stack loading problem and is the first study on the load-bearing limit case. The load-bearing limit is a fundamental constraint but has not been taken into account in studies in the stack loading problem. Based on ISO Standard 1496, the corner posts and corner fittings of ISO Series I containers can bear a certain amount of weight. If the total weight of the containers above exceeds the load-bearing limit of the lower container, it will hazard the load-bearing safety. This paper proposes two problem formulations: three-index formulation and two-index formulation. The three-index formulation adds the load-bearing limit to the existing stack loading problem formulation. It turns out that the traditional three-index formulation of the stack loading problem is not efficient when being used in solving the problem with load-bearing constraints. Therefore, we propose a new two-index formulation. Apart from the theoretical results, this paper proposes a matheuristic solution framework: firstly, using binary search with greedy matheuristic for feasibility checking to minimize the number of stacks, and secondly, using tabu search matheuristic to minimize the number of unordered stackings. In future research, we will apply the matheuristic to different types of container scenarios and the parallel stack loading case.

Mobile crowdsourcing has emerged as a popular approach for organizations to leverage the collective intelligence of a crowd of users to obtain services. Considering users’ costs for providing services, it is vital for the requester to design incentive mechanisms to encourage users’ participation in crowdsourcing under the budget constraint. This aligns with the concept of budget-feasible mechanism design. Existing budget-feasible mechanisms often assume immediate user reachability and willingness of joining the crowdsourcing, which is unrealistic. To address this issue, a promising approach is to have participating users diffuse auction information to potential users in the social network. However, this brings another challenge in that participating users can be strategic and therefore hesitant to invite more potential competitors to join the crowdsourcing platform. In this paper, we focus on developing diffusion mechanisms that incentivize strategic users to actively diffuse auction information through the social network. This helps to attract more informed users and ultimately increases the value of the procured services. Specifically, we propose optimal budget-feasible diffusion mechanisms that simultaneously guarantee individual rationality, budget-feasibility, strong budget-balance, incentive-compatibility (i.e., users report real costs and diffuse auction information to all their neighbors) and approximation. Experiment results under real datasets further demonstrate the efficiency of proposed mechanisms.

The industrial wireless field network plays a critical role in industrial internet of things(IIoT) for various categories of industrial applications. However the channel resource utilization under such a kind of network faces the significant challenges due to the natural time-variance of channel state and its communication quality. As such we propose a transmission capacity exploiting scheme to maximize the channel data rate and its transmission capacity as well for each communication channel. Based on it we present a channel bandwidth resource utilization optimization scheme to achieve the desired network transmission performance objectives. Particularly an OFDMA multi-hop multi-channel based joint resource scheduling scheme is proposed to optimize network system transmission throughput with respect to the designed utility function for each type of user traffic flows over the network. We proposed computationally efficient polynomial-time scheduling algorithms to deal with both QoS and Non-QoS traffic flows with the consideration of both multi-user diversity, QoS performance demand and proportional fairness requirement as well. The experimental results also validate the superior performance of the proposed scheme in industrial wireless field networks.

Real-time end-to-end task scheduling in networked control systems (NCSs) requires the joint consideration of both network and computing resources to guarantee the desired quality of service (QoS). This paper introduces a new model for composite resource scheduling (CRS) in real-time networked control systems, which considers a strict execution order of sensing, computing, and actuating segments based on the control loop of the target NCS. We prove that the general CRS problem is NP-hard and study two special cases of the CRS problem. The first case restricts the computing and actuating segments to have unit-size execution time while the second case assumes that both sensing and actuating segments have unit-size execution time. We propose an optimal algorithm to solve the first case by checking the intervals with 100% network resource utilization and modify the deadlines of the tasks within those intervals to prune the search. For the second case, we propose another optimal algorithm based on a novel backtracking strategy to check the time intervals with the network resource utilization larger than 100% and modify the timing parameters of tasks based on these intervals. For the general case, we design a greedy strategy to modify the timing parameters of both network segments and computing segments within the time intervals that have network and computing resource utilization larger than 100%, respectively. The correctness and effectiveness of the proposed algorithms are verified through extensive experiments.

Traditional methods of annotating the sentiment of an unlabeled document are based on sentiment lexicons or machine learning algorithms, which have shown low computational cost or competitive performance. However, these methods ignore the semantic composition problem displaying in several ways such as negative reversing and intensification. In this paper, we propose a new method for sentiment classification using negative and intensive sentiment supplementary information, so as to exploit the linguistic feature of negative and intensive words in conjunction with the context information. Particularly, our method can solve the domain-specific problem without relying on the external sentiment lexicons. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method.

Maintaining the temporal validity of real-time data in cyber-physical systems is of critical importance to ensure the correct decision making and appropriate system operation. Most existing work on real-time data retrieval assume that the real-time data under study are always available for retrieval, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity constraints. This assumption, however does not hold in many real-time applications with intermittent data availability. In this paper, we study the Availability-constrained Fresh Data Retrieval (AFDR) problem, which aims to retrieve all required real-time data for a given set of decision tasks on time while taking both the temporal validity and data availability constraints into consideration. We formulate the AFDR problem as an ILP problem and study its complexity under different settings. Given the general case of the AFDR problem is proved to be NP-hard, we focus on the cases that data items have unit-size retrieval time. For the single decision task scenario, we propose a polynomial-time optimal data retrieval algorithm, which consists of a task finish time selection phase and an optimal retrieval schedule construction phase, to solve the AFDR problem. For the multiple decision task scenario, we propose an efficient heuristic algorithm by transforming the temporal validity constraint of a real-time data item to the availability constraint. The effectiveness of the proposed algorithms has been validated through extensive experiments. Our results show that the heuristic algorithm outputs around $1.5\times$1.5× feasible cases compared to that of the state-of-the-art scheme.

Real-time task scheduling for wireless networked control systems provides guarantees for the quality of service. This paper introduces a new model for joint network and computing resource scheduling (JNCRS) in real-time wireless networked control systems. This new end-to-end real-time task model considers a strict execution order of segments including the sensing, the computing and the actuating segment based on the control loop of WNCSs. The general JNCRS problem is proved to be a NP-hard problem. After dividing the JNCRS problem into four subproblems, we propose a polynomial-time optimal algorithm to solve the first subproblem where each segment has unit execution time, by checking the intervals with 100% network resource utilization and modify the deadlines of tasks. To solve the second subproblem where the computing segment is larger than one unit execution time, we define the new timing parameters of each network segment by taking into account the scheduling of the computing segments. We propose a polynomial-time optimal algorithm to check the intervals with the network resource utilization larger than or equal to 100% and modify the timing parameters of tasks based on these intervals.

Motivation: RNA virus populations contain different but genetically related strains, all infecting an individual host. Reconstruction of the viral haplotypes is a fundamental step to characterize the virus population, predict their viral phenotypes and finally provide important information for clinical treatment and prevention. Advances of the next-generation sequencing technologies open up new opportunities to assemble full-length haplotypes. However, error-prone short reads, high similarities between related strains, an unknown number of haplotypes pose computational challenges for reference-free haplotype reconstruction. There is still much room to improve the performance of existing haplotype assembly tools. Results: In this work, we developed a de novo haplotype reconstruction tool named PEHaplo, which employs paired-end reads to distinguish highly similar strains for viral quasispecies data. It was applied on both simulated and real quasispecies data, and the results were benchmarked against several recently published de novo haplotype reconstruction tools. The comparison shows that PEHaplo outperforms the benchmarked tools in a comprehensive set of metrics. Availability and implementation: The source code and the documentation of PEHaplo are available at https://github.com/chjiao/PEHaplo. Supplementary information: Supplementary data are available at Bioinformatics online.

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach.

Nowadays, memory energy reduction attracts significant attention as main memory consumes large amount of energy among all the energy consuming components. This paper focuses on reducing the energy consumption of the shared main memory in multicore processors by putting the memory into sleep state when all cores are idle. Based on this idea, we present systematic analysis of different models and propose a series of scheduling schemes to maximize the common idle time of all cores. The target problem is classified into two cases based on whether task migration is allowed or not among cores. Considering task migration, an optimal scheduling scheme is proposed, assuming the number of cores is unbounded. When the number of cores is bounded, an integer linear programming formulation and two efficient heuristic algorithms are proposed. When task migration is not allowed, we first prove the NP-hardness of the problem, and then propose the optimal solutions when task partitions are given in advance. The energy overhead caused by transitions between active and sleep modes of the memory is analyzed. The experimental results show that the heuristic algorithms work efficiently and can save 7.25% and 11.71% system energy, respectively, with 1-GB memory, compared with an energy-efficient multicore scheduling scheme. Larger energy reduction can be further achieved with larger size of memory.

Streaming services are gaining popularity and have contributed a tremendous fraction of today's cellular network traffic. Both playback fluency and battery endurance are significant performance metrics for mobile streaming services. However, because of the unpredictable network condition and the loose coupling between upper layer streaming protocols and underlying network configurations, jointly optimizing rebuffering time and energy consumption for mobile streaming services remains a significant challenge. In this paper, we propose a novel framework that effectively addresses the above limitations and optimizes video transmission in cellular networks. We design two complementary algorithms, Rebuffering Time Minimization Algorithm (RTMA) and Energy Minimization Algorithm (EMA) in this framework, to achieve smoothed playback and energy-efficiency on demand over multi-user scenarios. Our algorithms integrate cross-layer parameters to schedule video delivery. Specifically, RTMA aims at achieving the minimum rebuffering time with limited energy and EMA tries to obtain the minimum energy consumption while meeting the rebuffering time constraint. Extensive simulation demonstrates that RTMA is able to reduce at least 68% rebuffering time and EMA can achieve more than 27% energy reduction compared with other state-of-the-art solutions.

One of the most fundamental tasks of wireless sensor networks is to provide coverage of the deployment region. We study the coverage of a line interval with a set of wireless sensors with adjustable coverage ranges. Each coverage range of a sensor is an interval centered at that sensor whose length is decided by the power the sensor chooses. The objective is to find a range assignment with the minimum cost. There are two variants of the optimization problem. In the discrete variant, each sensor can only choose from a finite set of powers, whereas in the continuous variant, each sensor can choose power from a given interval. For the discrete variant of the problem, a polynomial-time exact algorithm is designed. For the continuous variant of the problem, NP-hardness of the problem is proved and followed by an ILP formulation. Then, constant-approximation algorithms are designed when the cost for all sensors is proportional to r κ for some constant κ ≥ 1, where r is the covering radius corresponding to the chosen power. Specifically, if κ = 1, we give a 1.25-approximation algorithm and a fully polynomial-time approximation scheme; if κ > 1, we give a 2-approximation algorithm. We also show that the approximation analyses are tight.

For 8-bit microcontrollers, bank-switching is commonly used to increase memory capacity. The disadvantage of this technique is that bank (page) selection instructions are introduced when switching active data (program) bank. The page selection problem is to minimize the number of page selection instructions inserted. While previous efforts work on optimizing bank selection instructions for the data segment, our work focuses on minimizing page selection instructions for the program segment. Minimizing page selection instructions is a more challenging problem as the size of each procedure being allocated is affected by the number of inserted page selection instructions. In this paper, we first give a formal definition of the page selection problem, and then we formulate the problem as an Integer Linear Programming (ILP) to find the optimal solution. We introduce a tabu search heuristic algorithm, TMSEARCH, to solve the problem efficiently. The experimental results show that ILP can find optimal solutions for small-scale problems, and TMSEARCH is able to find good solutions for all benchmarks within reasonable time. Com-pared to a commercial compiler, TMSEARCH reduces total code size between 0.04% and 19.3%, and reduces page selection instructions between 24.3% and 78.7%.

In this paper, we consider the task allocation problem on a hybrid main memory composed of nonvolatile memory (NVM) and dynamic random access memory (DRAM). Compared to the conventional memory technology DRAM, the emerging NVM has excellent energy performance since it consumes orders of magnitude less leakage power. On the other hand, most types of NVMs come with the disadvantages of much shorter write endurance and longer write latency as opposed to DRAM. By leveraging the energy efficiency of NVM and long write endurance of DRAM, this paper explores task allocation techniques on hybrid memory for multiple objectives such as minimizing the energy consumption, extending the lifetime, and minimizing the memory size. The contributions of this paper are twofold. First, we design the integer linear programming (ILP) formulations that can solve different objectives optimally. Then, we propose two sets of heuristic algorithms including three polynomial time offline heuristics and three online heuristics. Experiments show that compared to the optimal solutions generated by the ILP formulations, the offline heuristics can produce near-optimal results.

In this paper, we utilize a hybrid main memory composed of DRAM and Phase Change Random Access Memory (PRAM) for DSP systems, which leverages the low power consumption of PRAM while minimizing the performance and endurance degradation caused by write operations on PRAM. We re-consider the variable partitioning problem on this hybrid main memory. Different objectives, for example power consumption and the number of writes on PRAM, are considered in this paper. By using the proposed models and algorithms, experiments show that we can reduce 53% power consumption and 79% the number of writes on PRAM on average, compared with pure DRAM and pure PRAM memory, respectively.

Nonvolatile memories such as Flash memory, phase change memory (PCM), and magnetic random access memory (MRAM) have many desirable characteristics for embedded systems to employ them as main memory. However, there are two common challenges we need to answer before we can apply nonvolatile memory as main memory practically. First, nonvolatile memory has limited write/erase cycles compared to DRAM. Second, a write operation is slower than a read operation on nonvolatile memory. These two challenges can be answered by reducing the number of write activities on nonvolatile main memory. In this paper, we proposed two optimization techniques, write-aware scheduling and recomputation, to minimize write activities on nonvolatile memory. With the proposed techniques, we can both speed up the completion time of programs and extend nonvolatile memory's lifetime. The experimental results show that the proposed techniques can reduce the number of write activities on nonvolatile memory by 55.71% on average. Thus, the lifetime of nonvolatile memory is extended to 2.5 times as long as before on average. The completion time of programs can be reduced by 56.67% on systems with NOR Flash memory and by 47.63% on systems with NAND Flash memory on average.

Cache is known for its unpredictability in embedded systems. Cache locking technique is often utilized to guarantee a tighter prediction of Worst-Case Execution Time (WCET) which is one of the most important performance metrics for embedded systems. However, in Multi-Processor Systems-on-Chip (MPSoC) systems with multi-tasks, Level 2 (L2) cache is often shared among different tasks and cores, which leads to higher complexity in the cache management and extended unpredictability of cache. Task assignment has inherent relevancy for cache behavior, while cache behavior also affects the efficiency of task assignment. Task assignment and cache behavior have dramatic influences on the overall WCET of MPSoC. In this paper, overall WCET represents the worst-case finishing time of a set of tasks running on different cores. This paper proposes joint task assignment and cache partitioning techniques to minimize the overall WCET for MPSoC systems. Cache locking is applied to each task to guarantee a precise WCET, which in return facilitates task assignment and cache partitioning. We prove that the joint problem is NP-Hard and propose several efficient algorithms. Experimental results show that the proposed algorithms can consistently reduce the overall WCET compared to previous techniques.

A large number of embedded systems include 8-bit microcontrollers for their energy efficiency and low cost. Multi-bank memory architecture is commonly applied in 8-bit microcontrollers to increase the size of memory without extending address buses. To switch among different memory banks, a special instruction, Bank Selection, is used. How to minimize the number of bank selection instructions inserted is important to reduce code size for embedded systems.

VLIW architectures have gained acceptance in embedded systems. Traditional monolithic register file is not suitable for VLIW architectures with a large number of functional units. Clustered VLIW architecture is often applied, where the register file is partitioned into a number of smaller register files. Register files represent a substantial portion of the energy consumption in modern processors, and it is growing rapidly with wider instruction width. Most of the known clustered VLIW architectures partition the register file evenly among clusters. In this paper, we study the effect of energy consumption with register file re-partitioning on clustered VLIW architecture, where register files are not necessarily partitioned evenly. We present algorithms to compute energy-efficient re-partition of register files under different conditions. The impact of different intercluster communication models as well as the impact of program behavior on the register file re-partitioning are analyzed in this paper. Experimental results show that energy saving can be achieved using the proposed techniques.

According to the characteristics of the "3-D" structure of contemporary DRAM chips, the row first column ordered (RFCO) algorithm is proposed in this paper to minimize memory access schedule length. In memory systems with a single memory controller, assuming that the memory access trace is known before scheduling, the RFCO algorithm can generate schedules which are 7.89% shorter than burst scheduling on average. If memory accesses are coming to the single memory controller in real time, the RFCO algorithm can generate schedules which are 8.03% shorter than burst scheduling on average.

This paper describes a transformational method applied to the core component of role-based access control (RBAC), to derive efficient implementations from a specification based on the ANSI standard for RBAC. The method is based on the idea of incrementally maintaining the result of expensive set operations, where a new method is described and used for systematically deriving incrementalization rules. We calculate precise complexities for three variants of efficient implementations as well as for a straightforward implementation based on the specification. We describe successful prototypes and experiments for the efficient implementations and for automatically generating efficient implementations from straightforward implementations.