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China is in the stage of rapid development of new urbanization, cities are more vulnerable when facing various risks, and the construction of urban resilience (UR) makes cities more secure. At the same time, the allocation of digital factors has a vital impact on the construction of UR under energy constraints. Based on the panel data of cities in China from 2011 to 2020, this paper studies the influence and mechanism of digital factor allocation (DFA) on UR under energy constraints through panel fixed effect model (FE), mediation effect model, and panel threshold model. The main empirical results include: (1) The misallocation of digital factors has a negative impact on UR. In the case of differences in the division of urban agglomeration, the impact of digital factor misallocation (DFM) on UR will also be different. (2) The misallocation of digital factors has an impact on UR through the intermediary effects of capital allocation efficiency, green innovation and industrial structure upgrading. (3) There is a nonlinear relationship between the DFM and UR. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal tracking performance of systems by considering packet dropouts, additive white Gaussian noise(AWGN), and coding under energy constraints. The optimal tracking performance of communication‐constrained systems is obtained by spectral decomposition and partial factorization. The results show that the optimal tracking performance of the system is related to intrinsic properties such as non‐minimum phase zeros and unstable poles. What's more, encoding, data loss rate, and AWGN are also able to affect the performance of the system. Finally, the correctness of the results is verified by specific examples. [ABSTRACT FROM AUTHOR]

Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter. [ABSTRACT FROM AUTHOR]

In this paper, the input-to-state stability (ISS) control problem is studied for cyber-physical systems (CPSs) in the presence of asynchronous denial-of-service (DoS) attacks. To enhance the exponential ISS, an intelligent packet-based control method with buffering is proposed by introducing the acknowledgement signal (ACK), in which the trial transmission attempts of the control packet are intelligently determined by the situation (successful or failed) of its last sampling transmission instant after identifying the unnecessary transmission points and the necessary transmission points. Then, inspired by the packet-based control technology with buffering, the sufficient condition for attempting transmission of the control packet is given, in which the exponential ISS with maximum robustness index can be preserved. Compared with the existing method with a fixed transmission rate for the control packet, it is shown that the considered framework improves the exponential ISS performance with equal or less communication resource costs. [ABSTRACT FROM AUTHOR]

This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber–physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0–1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method. • A novel energy constrained fusion attack model is proposed as the linear combination of transmitted innovations. • Under the proposed attack framework, the remote state estimation errors are analyzed. • By analyzing the structure of the objective function, the optimal attack scheme is found by solving a multivariate SDP problem and a linear 0–1 programming problem. [ABSTRACT FROM AUTHOR]

This paper is concerned with the leader-follower consensus of multiagent systems with wireless communications with the main objective of reducing the power consumption. First, by assuming that the sampling period jumps from one to another only from a given set, a new stochastic sampling approach is introduced to reduce the sampling frequency of each agent. Then, only 1-D of the sampled data is selected, and transmitted to its neighboring agents. Finally, each agent is scheduled to communicate with others intermittently. A unified Markovian system model is proposed to capture the above stochastic sampling, measurement selection scheme and intermittent transmission process, and such a novel protocol can significantly reduce the power consumption. Based on the Lyapunov stability theory and the Markovian jump system approach, the distributed consensus-based controller gain is obtained by solving an optimization problem. The advantage of the proposed consensus protocol is verified by two simulation examples. The simulation results explicitly show our result is more energy-efficient than that of existing one. [ABSTRACT FROM PUBLISHER]

It has been shown that the heating, ventilation, and air conditioning (HVAC) systems of commercial buildings can offer ancillary services to power systems without loss of comfort. In this paper, we propose a new control framework for reliable scheduling and provision of frequency reserves by aggregations of commercial buildings. The framework incorporates energy-constrained frequency signals, which are adopted by several transmission system operators for loads and storage devices. We use a hierarchical approach with three levels: 1) reserve capacities are allocated among buildings (e.g., on a daily basis) using techniques from robust optimization; 2) a robust model predictive controller optimizes the HVAC system consumption typically every 30 minutes; and 3) a feedback controller adjusts the consumption to provide reserves in real time. We demonstrate how the framework can be used to estimate the reserve capacities in simulations with typical Swiss office buildings and different reserve product characteristics. Our results show that an aggregation of approximately 100 buildings suffices to meet the 5-MW minimum bid size of the Swiss reserve market. [ABSTRACT FROM PUBLISHER]

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network. [ABSTRACT FROM PUBLISHER]

• The established deep learning model is capable of considering the impacts of 3D heterogeneity of oil shale on production and energy consumption. • The optimization procedure simultaneously optimized well constraints and well locations. • The evolution patterns of optimal parameter values across varying levels of energy constraints are identified. The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP. The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage. [ABSTRACT FROM AUTHOR]

This paper addresses a scheduling problem with a cumulative continuous resource and energy constraints. Given a set of non-preemptive tasks, each task requires a continuously-divisible resource. The instantaneous resource usage of a task is limited by a minimum and maximum resource requirement. Its processing has to lie within a time-window and the total energy received obtained by integrating a function f of the instantaneous resource usage over the processing interval must reach a required value (where f is a non-decreasing, continuous function). The problem is to find a feasible schedule of the tasks, which satisfies all the constraints. This problem, which is a generalization of the well-known cumulative scheduling problem, is NP-complete. For the case where all functions f are linear, we exhibit structural properties of the feasible solutions and we present a Mixed Integer Linear Program (MILP) based on an event-based formulation. We also adapt the famous 'left-shift/right-shift' satisfiability test (energetic reasoning) and the associated time-window adjustments to our problem. To achieve this test, we present three different ways for computing the relevant intervals. Finally, we present a hybrid branch-and-bound method to solve the problem, which performs, at each node, the satisfiability test and time-window adjustments and, when the domains of all start and end times are small enough, the remaining solution space is searched via the event-based MILP. [ABSTRACT FROM AUTHOR]

The advancements of technology in the field of communication made WSN based IoT attractive and applicable to various areas. It is comprised IoT nodes that work on limited battery supplies. Hence, a high-performance routing protocol is essential for routing in such networks to overcome the energy constraint problems. In this paper, an energy efficient routing algorithm Fractional Gravitational Grey Wolf Optimization (FGGWO) is proposed for multipath data transmission. This work is motivated by the Ant Colony Optimization (ACO) algorithm that discovered multipaths based on clustering technique. The proposed algorithm improves the routing process of ACO in a two stage process. At first, the cluster heads are selected by utilizing the previous work Fractional Gravitational Search Algorithm (FGSA). Secondly, multiple paths are generated from the source to the destination using FGGWO, which modifies Grey Wolf Optimization by integrating FGSA in the algorithm. Objectives, such as, energy, inter and intra-cluster distance, delay and lifetime, considered in the fitness function provide optimal paths for the transmission. The experimental results show that the proposed FGSA + FGGWO algorithm has higher performance regarding energy and alive nodes, in comparison with the existing ABC + ACO, FABC + EACO, and Threshold + ACO techniques. The maximum number of alive nodes and energy estimated in FGSA + FGGWO is 25 and 0.1298 for 50 nodes; and 27 and 0.0876, for 100 nodes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature. [ABSTRACT FROM AUTHOR]

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles. [ABSTRACT FROM AUTHOR]

This paper addresses a scheduling problem with a continuously divisible, cumulative and renewable resource with limited capacity. During its processing, each task consumes a part of this resource, which lies between a minimum and a maximum requirement. A task is finished when a certain amount of energy is received by it within its time window. This energy is received via the resource and an amount of resource is converted into an amount of energy with a non-decreasing and continuous function. The goal is to find a feasible schedule, which is already NP-complete, and then to minimize the resource consumption. For the case where all functions are linear, we present two new mixed-integer linear programs (MILP), as well as improvements of an existing formulation. We also present a detailed version of the adaptation of the well-known 'left-shift/right-shift' satisfiability test for the cumulative constraint and the associated time-window adjustments to our problem. For this test, three ways of computing relevant intervals are described. Finally, a hybrid branch-and-bound using both the satisfiability test and the MILP is presented with a new heuristic for choosing the variable on which the branching is done. Computational experiments on randomly generated instances are reported in order to compare all of these solution methods. [ABSTRACT FROM AUTHOR]

Aiming at the limitation of various resources such as energy, bandwidth and memory in a wireless sensor network ( WSN), this paper proposed a data reduction combination prediction method based on XGBoost and hierarchical fractional least-mean-square( HFLMS) Firstly, it used the XGBoost method to perform a second-order Taylor expansion of the loss function, which balanced the complexity of the model and the decline rate of the loss function, and achieved the stable prediction of the resource limit. Then, it employed the proposed HFLMS for data reduction in WSN, and used error estimation to predict the measured data, which would reduce the energy constraints in WSN. Finally, it used the two evaluation parameters ( energy and prediction error) to evaluate the performance of the proposed prediction method. The experimental results demonstrate that the proposed prediction method is better than that without prediction, the approximate steepest descent algorithm and the layered minimum mean square filtering technology. [ABSTRACT FROM AUTHOR]

In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithmic problems for quantitative models that are motivated by the applications in modeling embedded systems. We consider two-player games played on a weighted graph with mean-payoff objective and with energy constraints. We present a new pseudopolynomial algorithm for solving such games, improving the best known worst-case complexity for pseudopolynomial mean-payoff algorithms. Our algorithm can also be combined with the procedure by Andersson and Vorobyov to obtain a randomized algorithm with currently the best expected time complexity. The proposed solution relies on a simple fixpoint iteration to solve the log-space equivalent problem of deciding the winner of energy games. Our results imply also that energy games and mean-payoff games can be reduced to safety games in pseudopolynomial time. [ABSTRACT FROM AUTHOR]

We consider a cumulative scheduling problem where a task duration and resource consumption are not fixed. The consumption profile of the task, which can vary continuously over time, is a decision variable of the problem to be determined and a task is completed as soon as the integration over its time window of a non-decreasing and continuous processing rate function of the consumption profile has reached a predefined amount of energy. The goal is to find a feasible schedule, which is an NP-hard problem. For the case where functions are concave and piecewise linear, we present two propagation algorithms. The first one is the adaptation to concave functions of the variant of the energetic reasoning previously established for linear functions. Furthermore, a full characterization of the relevant intervals for time-window adjustments is provided. The second algorithm combines a flow-based checker with time-bound adjustments derived from the time-table disjunctive reasoning for the cumulative constraint. Complementarity of the algorithms is assessed via their integration in a hybrid branch-and-bound and computational experiments on small-size instances. [ABSTRACT FROM AUTHOR]

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy. [ABSTRACT FROM AUTHOR]

This study presents a closed‐loop coordination mode and model between generation maintenance scheduling (GMS) and long‐term security‐constrained unit commitment (SCUC) considering energy constraints and N − 1 contingencies that aims at improving the security and economy of power system operations. Given the calculation complexity, this study employs constraint transformation techniques and an efficient approach termed the relaxation induced method, which is based on the solution of the relaxed mixed‐integer programming (MIP) model. The proposed approach can quickly obtain a near‐optimal solution. If the near‐optimal solution is not acceptable for system operators, it can be used to warm‐start the solution of the original MIP problem. The modified IEEE 30‐bus test system and a provincial power system in China are employed to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art. [ABSTRACT FROM PUBLISHER]

An efficient method to solve the long-term security constrained unit commitment (SCUC) is a requisite for power system operators in many countries, particularly those with many coal-fired generating units, which require a long time to start up and shut down. However, the complexity that is introduced by the energy or fuel constraints in the long-term SCUC problem often results in exorbitant computation time. In this paper, a new type of approach, which is termed the inducing-objective-function (IOF)-based method, is proposed. This method significantly improves the computational efficiency. In the proposed method, the inducing factors are introduced into the objective function of the SCUC relaxation problem to induce more UC state variables to satisfy the integrality requirement after several iterations. According to the relaxation solution, the objective function of the original SCUC model is modified. Thus, the process of solving the modified model using the branch-and-cut algorithm can be more efficient. Because the solution of this modified model can be near-optimal for the original model, the gap between the near-optimal and the optimal solution is estimated to decide whether the near-optimal solution is acceptable for practical application. If the near-optimal solution is not acceptable, it will be used as the initial solution to solve the original model. Because the initial solution is of high quality, it can significantly speed up the branch-and-cut process. Two case studies with different scales demonstrate the robustness, the efficiency and the prospect of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

The fusion estimation issue of sensor networks is investigated for nonlinear time-varying systems with energy constraints, time delays as well as packet loss. For the addressed problem, some local estimations are first obtained by using the designed Luenberger-type local estimator and then transmitted to a fusion center (FC) to generate a desired fusion value. A novel transmission model with energy constraints is proposed, where part information is reliably transmitted and the other is randomly determined whether to be transmitted. Furthermore, a diagonal matrix is utilized to describe the communication scheduling. With the help of the Lyapunov stability theory, sufficient conditions are established to ensure the predetermined local and fused H ∞ performances over a finite horizon. Furthermore, by virtue of the well-known Schur complement lemma, the desired gains of local estimators and the suboptimal fusion weight matrices are obtained in light of the solution of linear matrix inequalities. It should be pointed out that the developed scheme is a two-step process under which the design of fusion weight matrices is based on the obtained estimator gains. Finally, a simulation example for sensor networks is performed to check the effectiveness of the proposed fusion scheme. [ABSTRACT FROM AUTHOR]

Recently, the security problem of wireless networked control systems (WNCSs) has become critical. In this article, we focus on strategies designing problem for sensor and denial-of-service (DoS) attacker in WNCS based on linear quadratic Gaussian (LQG) cost. In this scenario, a sensor measures the output of the system and transmits its local state estimate to the remote estimator via an unreliable channel which may suffer interference from an intelligent DoS attacker. In each step, the sensor demands to determine the power to transmit its packet data, at the same time, the attacker needs to determine the interference power to degrade the performance of the WNCS. To analyze this interactive decision-making process, we construct a two-player zero-sum stochastic game and propose an improved LQG cost considering energy-efficient factors. Then, for continuous power-level setting, we discuss the existence and the structure of equilibrium strategies of the sensor–attacker game. For a discrete power set, an algorithm is developed to solve equilibrium strategies for both players. Finally, we present an example to illustrate our results. [ABSTRACT FROM AUTHOR]

Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training framework. As wireless devices involved in FEEL are resource limited in terms of communication bandwidth, computing power and battery capacity, it is important to carefully schedule them to optimize the training performance. In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance within the energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are considered. The consideration of computation energy makes dynamic scheduling challenging, as devices are scheduled before local training, but the communication energy for over-the-air aggregation depends on the $l_{2}$ -norm of local gradient, which is known only after local training. We thus incorporate estimation methods into scheduling to predict the gradient norm. Taking the estimation error into account, we characterize the performance gap between the proposed algorithm and its offline counterpart. Experimental results show that, under a highly unbalanced local data distribution, the proposed algorithm can increase the accuracy by 4.9% on CIFAR-10 dataset compared with the myopic benchmark, while satisfying the energy constraints. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we study the problem of stabilizing a linear system with a single long time-varying delay in the input. Under the assumption that the open-loop system is stabilizable and not exponentially unstable, a finite dimensional static time-varying linear state feedback controller is obtained by truncating the predictor based controller and by adopting the parametric Lyapunov equation based controller design approach. As long as the time-varying delay is exactly known and bounded, an explicit condition is provided to guarantee the stability of the closed-loop system. It is also shown that the proposed controller achieves semi-global stabilization of the system if its actuator is subject to either magnitude saturation or energy constraints. Numerical examples show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

The fundamental objective of deploying wireless sensors is to monitor a physical condition(s) within the desired region and relay the measured data to the interested parties through a gateway. However, achieving maximum coverage over the desired area after the initial random deployment of nodes is likely impossible. Arbitrary paths become exposed, uncovered by any of the sensor nodes questioning the quality of service of the network. In our proposed coverage maximization scheme, exposure profiles of sensor nodes were used by the sink node to compute the minimal exposure path. A combination of the computed minimal exposed path and the ratio of covered to uncovered grids were used as the function fitness in our particle swarm optimization algorithm. Bearing in mind the energy constraints of sensor nodes, randomly deployed nodes were only repositioned in the optimization process when their energy levels are above the predefined threshold. The accuracy and efficiency of our scheme are established via simulations. We compared the results of our scheme with a similar optimization scheme. The results showed our scheme achieved a higher coverage degree with the same number of deployed nodes over a region of interest of the same size. [ABSTRACT FROM AUTHOR]

We are concerned with conservative systems q = ∇V(q), q ∈ RN for a general class of potentials V ∈ C1(RN). Assuming that a given sublevel set {V ≤ c} splits in the disjoint union of two closed subsets Vc− and Vc+, for some c ∈ R, we establish the existence of bounded solutions qc to the above system with energy equal to −c whose trajectories connect Vc− and Vc+. The solutions are obtained through an energy constrained variational method, whenever mild coerciveness properties are present in the problem. The connecting orbits are classified into brake, heteroclinic or homoclinic type, depending on the behavior of ∇V on ∂Vc±. Next, we illustrate applications of the existence result to double-well potentials V, and for potentials associated to systems of duffing type and of multiple-pendulum type. In each of the above cases we prove some convergence results of the family of solutions (qc). [ABSTRACT FROM AUTHOR]

Energy constraints of sensor nodes in wireless sensor networks (WSNs) is a major challenge and minimising the overall data transmitted across a network using data aggregation, distributed source coding, and compressive sensing have been proposed as mechanisms for energy saving. Similarly, use of mobile nodes capable of relocating within the network has been widely explored for energy saving. In this study, the authors propose a novel method for using miniature aerial vehicles for data collection instead of actively sensing from a deployed network. The proposed mechanism is referred as data collection fly (DCFly). It is suitable for data collection from WSNs deployed in harsh‐undulating terrain with the base station located far from the sensing region. The DCFly is compared with data collection based on multi‐hop data aggregation and data collection with mobile sinks. The numerical results justify that the proposed data collection mechanism is effective and efficient for use in WSNs. [ABSTRACT FROM AUTHOR]

In wireless multi-hop packet radio networks (MPRNs) that employ geographic transmissions, sleep schedules or node activation techniques may be used to power off some nodes to conserve energy. We consider the problem of selecting which nodes should power on to listen to a scheduled transmission when the channel suffers from random fading. We choose the objective of maximizing the expected value of the distance covered in a single transmission between a transmitter and the farthest receiver that successfully receives the packet, under a constraint on the expected number of receivers that turn on. Since there is a tradeoff between the distance of a node from the transmitter and the probability that the node receives the message correctly, we propose to use node-activation based on link-distance (NA-BOLD). We investigate optimal and sub-optimal NA-BOLD schemes and compare their performance with that of schemes that use a constant sleep schedule for every node within some radius of the transmitter. Our results show that the proposed NA-BOLD schemes achieve significantly larger transmission distances than conventional schemes. [ABSTRACT FROM AUTHOR]

Energy is the basic support of China's fast economic growth, industrialization and modernization, but China has suffered from an energy shortage. We examine the role of energy development in China, and then assess the effect that energy shortages have on economic development in the short-term. We suggest some policies for energy saving and development in the long-term. [ABSTRACT FROM AUTHOR]

There is significant interest in battery-powered sensor networks to be used for detection in a wide variety of applications, from surveillance and security to health and environmental monitoring. Severe energy and bandwidth constraints at each sensor node demand system-level approaches to design that consider detection performance jointly with system-resource constraints. Our approach is to formulate detection problems with constraints on the expected cost arising from transmission (sensor nodes to a fusion node) and measurement (at each sensor node) to address some of the system-level costs in a sensor network. For a given resource constraint, we find that randomization over the choice of measurement and over the choice of when to transmit achieves the best performance (in a Bayesian, Neyman-Pearson, and Ali-Silvey sense). To facilitate design, we describe performance criteria in the send/no-send transmission scenario, where the joint optimization over the sensor nodes decouples into optimization at each sensor node. [ABSTRACT FROM AUTHOR]

This study proposes a set of novel random access protocols combining Packet Repetition (PR) schemes, such as Contention Resolution Diversity Slotted Aloha (CRDSA) and Irregular Repetition SA (IRSA), with Non Orthogonal Multiple Access (NOMA). Differently from previous NOMA/CRDSA and NOMA/IRSA proposals, this work analytically derives the energy levels considering two realistic elements: the residual interference due to imperfect Interference Cancellation (IC), and the presence of requirements on the power spent for the transmission. More precisely, the energy-limited scenario is based on the relationship between the average available energy and the selected code modulation pair, thus being of specific interest for the implementation of the Internet of Things (IoT) technology in forthcoming fifth-generation (5G) systems. Moreover, a theoretical model based on the density evolution method is developed and numerically validated by extensive simulations to evaluate the limiting throughput and to explore the actual performance of different NOMA/PR schemes in energy-constrained scenarios. [ABSTRACT FROM AUTHOR]

We performed a food provisioning experiment in a population of Pied Flycatchers Ficedula hypoleuca breeding at high altitude in central Spain to test if food availability before and during laying determines clutch size. Food was provided to one of two pairs with the same date of initiation of nest-building (15 dyads of subsequently reproducing pairs were thus created). Food provisioning began on the day of initiation of nest-building and ended on the day after the last egg was laid. Although laying date was unaffected by the experiment, clutch size in the experimental treatment was significantly larger. This result could indicate that food availability at laying (1) proximately constrained clutch size or (2) that females evaluated future conditions for incubating eggs and feeding nestlings based on food availability at laying. Reproductive success (proportion of eggs that resulted in fledged young) was significantly reduced in the experimental treatment. This effect suggest that supplemented females were tricked by the experiment into laying more eggs than the number of eggs they were able to incubate with success and the number of nestlings they were able to feed, a source of error in clutch size adjustment which could be common in non-experimental situations. [ABSTRACT FROM AUTHOR]

The carrying capacity of the environment for a population is one of the key concepts in ecology and it is incorporated in the growth term of reaction-diffusion equations describing populations in space. Analysis of reaction-diffusion models of populations in heterogeneous space have shown that, when the maximum growth rate and carrying capacity in a logistic growth function vary in space, conditions exist for which the total population size at equilibrium (i) exceeds the total population that which would occur in the absence of diffusion and (ii) exceeds that which would occur if the system were homogeneous and the total carrying capacity, computed as the integral over the local carrying capacities, was the same in the heterogeneous and homogeneous cases. We review here work over the past few years that has explained these apparently counter-intuitive results in terms of the way input of energy or another limiting resource (e.g., a nutrient) varies across the system. We report on both mathematical analysis and laboratory experiments confirming that total population size in a heterogeneous system with diffusion can exceed that in the system without diffusion. We further report, however, that when the resource of the population in question is explicitly modeled as a coupled variable, as in a reaction-diffusion chemostat model rather than a model with logistic growth, the total population in the heterogeneous system with diffusion cannot exceed the total population size in the corresponding homogeneous system in which the total carrying capacities are the same. [ABSTRACT FROM AUTHOR]

Due to hardware, energy, cost and other physical constraints, sensor-based networks present various design, implementation and deployment challenges. An analytical model is presented to estimate and evaluate the node and network lifetime in a randomly deployed multi-hop sensor network. Based on this, we provide a procedure for the creation of an energy efficient sensor network organization, that attempts to extend the lifetime of the communication critical nodes, and as a result the overall network's operational lifetime. [ABSTRACT FROM PUBLISHER]