Search

Every node of an edge-weighted tree network contains a data packet. At some nodes are placed mobile agents, each one possessing an amount of energy (not necessarily the same for all agents). While walking along the network, the agents spend the energy proportionally to the distance traveled and collect copies of the data packets present at the visited network nodes. An agent visiting a node deposits there copies of all currently possessed data packets and collects a copy of every data packet present at this node. Two agents meeting at a node may exchange any amount of currently possessed energy. The gossiping problem asks whether it is possible to achieve that a copy of the original data packet of each node may reach every other node of the network. We prove that the gossiping problem can be solved in time O ( k 2 n 2 ) for an n-node tree network T, where k is the number of agents. Moreover, we prove that in order to compute a gossiping strategy, it is enough to start with a minimum-cost convergecast that ends with all data packets and all remaining energy being present at some node of T, and then finish with a minimum-cost broadcast from this configuration. Thus, we obtain two structural properties of the gossiping problem. First, if a gossiping is feasible and r is the first node receiving all information, then there is one that is a concatenation of a convergecast to r and a broadcast from r. Secondly, it is sufficient to consider only optimal convergecast strategies. This is natural to expect but hard to prove, as locations of agents after a convergecast (moved in this stage) are essential. Hence, the convergecast has to be optimal with regards to both spent energy as well as the resulting configuration of agents, which matters in the next stage.

Battery-less IoT devices powered through energy harvesting face a fundamental imbalance between the potential volume of collected data and the amount of energy available for processing that data locally. We explore a combination of approximate computing and intermittent computing-incidental approximate architecture to suit nonvolatile processors (NVPs).

One of the major issues in wireless sensor network is to reduce the energy consumption and to improve the network lifetime. Present work describes about the maximum a posterior estimate based V-BLAST transmission is used to improve the network life time of energy constrained networks. Here the sensors are randomly distributed and for data transmission V-BLAST method is used. Before transmitting data, the source node has to choose cooperative nodes and at each hop the selection of cooperative nodes is necessary. For proper selection of cooperative nodes, both prior and posterior probabilities are considered. The posterior probability is calculated with the knowledge of prior probability. The prior parameters are residual energy ( $$ {\text{R}}_{\text{e }} $$ ) and distance ( $$ {\text{D}}_{\text{t }} $$ ) of the intermediate nodes. The post conditions are channel interference ( $$ {\text{C}}_{\text{i }} $$ ), delay for transmitting ( $$ {\text{T}}_{\text{d }} $$ ) data to intermediator node, overload ( $$ {\text{O}}_{\text{l }} $$ ) at an intermediator node.

Toxic gas leakage that leads to equipment damage, environmental effects, and injuries to humans is the key concern in large-scale industries, particularly in petrochemical plants. Industrial wireless sensor networks (IWSNs) are specially designed for industrial applications with improved efficiency, and remote sensing for toxic gas leakage. Sleep scheduling is a common approach in IWSNs to overcome the network lifetime problem due to energy constrained nodes. In this article, we propose a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining global network connectivity with minimal awake nodes. Unlike the previous sleep scheduling algorithm, for example, the connected k-neighborhood (CKN)-based approach that wakes up the sleep nodes over the entire sensing field by increasing the k-value, our proposed scheme dynamically wakes up the sleep nodes only in the particular toxic gas leakage area. Simulation results show that our proposed scheme outperforms the CKN-based sleep scheduling scheme with the same required coverage degree for the toxic gas leakage area. In addition, the proposed scheme considers multiple hazardous zones with various coverage degree requirements. We show that at the expense of a slight extra message overhead, energy consumption in terms of totally awake nodes over the entire sensing field is reduced compared to the other approaches, while maintaining network connectivity.

Routing protocols for cognitive radio ad hoc networks (CRNs) select a route between the source and destination nodes based on the spectrum opportunity at intermediate nodes. When multiple routes are possible, most routing protocols for CRNs use some metric—independent of the traffic class—to select routes. However, a route that works well for transferring a particular class of data may not be the right one for a different data class, as its quality of service (QoS) requirements may differ. In this paper, we propose a reactive energy efficient routing protocol with differentiated services (REEDS) for cognitive radio networks. Route selection in REEDS is based on different (multiple) hop metrics calculated dynamically for different traffic classes so that a minimum level of QoS is guaranteed. Another characteristic feature of REEDS is the prediction and dodging of nodes that may be excessively loaded with traffic. This results in the avoidance of formation of holes due to heavy energy expenditure by some nodes. Simulation shows that routes in REEDS are established so that the QoS requirements of each traffic class are satisfied and lesser energy is consumed compared to other routing protocols for CRNs.

Modern Distributed Embedded Systems (DESs) tend to be more connected to other peers or external networks. This opens the gate for potential security attacks, although it is beneficial for embedded applications running on DESs. Security protections in DESs require significant time and energy overhead. Longer execution time rises the possibility of deadline violation of real-time applications, which may cause serious consequences in hard real-time systems. Energy efficiency is required since energy budget is usually very scarce under the context of DESs. Therefore, it is of critical importance to study the trade-off between security protection and corresponding timing and energy overheads so that the overall system performance can be maximally improved.In this paper, we approach the design of energy- and security-critical distributed real-time embedded systems from the early mapping and scheduling phases. We focus on providing the best confidentiality protection of internal communication in DESs under timing and energy constraints. The complexity of finding the optimal solution grows exponentially as the problem size increases. Therefore, we propose an efficient, genetic algorithm based heuristic to solve the problem. In the presented algorithm, solutions are evolved gradually so that good solutions can be obtained. Extensive experiments, including a real-life case study, demonstrate the efficiency of the proposed technique. Establish the model of security-critical distributed applications.Identify two strategies to improve the system security.Propose a security metric to quantify security quality of distributed applications.Design a GA based heuristic to address the design and optimization problem.Evaluate the proposed algorithm by extensive experiments and a real-life application.

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: (i) reserve capacities are allocated among buildings (e.g., on a daily basis) using techniques from robust optimization, (ii) a robust model predictive controller optimizes the HVAC system consumption typically every 30 minutes, and (iii) 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., 29 pages, 8 figures, 3 tables, submitted to the IEEE Transactions on Power Systems

In energy-constrained cognitive radio networks (CRNs), the choice on single channel access versus switched multichannel access is critical for energy saving and sustainable network operation. In this study, the authors study and compare the energy efficiency of switched (probabilistic) multichannel access (pMCA) and fixed single-channel access (SCA) in CRNs. In pMCA, a secondary user (SU) switches channel with certain probability whenever it encounters a busy channel. In SCA on the other hand, the SU stays on the same channel for its usage and waits for its availability. Via an analytical framework the authors derive the channel utilisation and energy efficiency of the two schemes. From the results the authors examine the primary user (PU) traffic dependent optimum switching probability in pMCA and the regime of PU activity dynamics where SCA outperforms pMCA.

To reduce the consumption of fossil fuel and greenhouse gas (GHG) emissions, incentive-based policies are used to encourage end-users to utilize more clean energy. Hydrogen energy is an ideal clean energy that can be integrated into the next generation power grid. Deferrable electrolyzers (DEs), as a typical electricity-to-hydrogen conversion devices and capable of modulating power consumption, can convert excessive power to store electricity as hydrogen. Therefore it can be used as a method for load management. The main contribution of this paper is to propose an energy-constrained state priority list (ECSPL) model, for analyzing the charging response of aggregated loads consisting of DE units. The typical hysteresis control of DEs as a load management mechanism is first discussed. A characteristic parameter, i.e. the energy state of DE charging load, is used to group and prioritize the DE units. The proposed ECSPL model optimally determines the operating status of DE charging and standby process, and it maintains the user-desired DE charging trajectory considering customer-constraints. The proposed model maintains the diversity of operating status of DE charging and standby process to prevent unexpected synchronization phenomenon for operating status. To evaluate the performance of the proposed method, an estimated baseline of the aggregated DE charging loads is obtained based on natural hysteresis control. The ECSPL control method of DE units for intra-hour load balancing is then evaluated. The effects of different energy-constraints, deadbands of sampled end-use state comparison, error associated with the charging-trajectory measurements are modeled to evaluate the performance of controlled DE group. The ECSPL model is described and demonstrated by the modeling results of investigated DE units.

Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

In this letter, we investigate the energy efficiency of multichannel spectrum access in cognitive radio networks. We consider two channel access schemes. In one case, a secondary user (SU) is assigned multiple channels and the channels are switched whenever a primary user (PU) returns. In the other, the SU operates on a single channel without switching to other channels. We develop an analytic framework on achievable channel utilization and energy consumption performance of the SU. From the results, we examine the trade-off between SU channel utilization and energy consumption, and identify the PU traffic conditions where one scheme outperforms the other.

Most routing algorithms in delay tolerant networks (DTNs) have not taken adequately into account the issue of energy constraint for communication. In this paper, we propose a new optimization strategy for different DTN routing algorithms that use an utility function to select the best nodes to relay messages, but that were originally proposed without regard to the issue of energy. Our proposed optimization strategy is based on the modeling of multi-copy forwarding as a Bayesian signaling game. A system for belief update concerning the energy of the other DTN nodes based on the accumulated observations of the destination nodes is considered. Simulation results show that our proposed optimization strategy can maintain the network nodes operational for a longer period of time.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

In recent years, using message ferries as mechanical carriers of data has been shown to be an effective way to collect information in sparse wireless sensor networks. As the sensors are far away from each other in such highly partitioned scenario, a message ferry needs to travel a long route to access all the sensors and carry the data collected from the sensors to the sink. Typically, practical constraints (e.g., the energy) preclude a ferry from visiting all sensors in a single tour. In such case, the ferry can only access part of the sensors in each tour and move back to the sink to get the energy refilled. So, the energy-constrained ferry route design (ECFRD) problem is discussed, which leads to the optimization problem of minimizing the total route length of the ferry, while keeping the route length of each tour below a given constraint. The ECFRD problem is proved to be NP-hard problem, and the integer linear programming (ILP) formulation is given. After that, efficient heuristic algorithms are proposed to solve this problem. The experimental results show that the performances of the proposed algorithms are effective in practice compared to the optimal solution.

Dynamic topology change and decentralized makes routing a challenging task in mobile ad hoc network. Energy efficient routing is the most challenging task in MANET due to limited energy of mobile nodes. Limited power of batteries typically use in MANET, and this is not easy to change or replace while running communication. Network disorder can occur for many factors but in middle of these factors deficiency of energy is the most significant one for causing broken links and early partition of the network. Evenly distribution of power between nodes could enhance the lifetime of the network, which leads to improving overall network transmission and minimizes the connection request. To discourse this issue, we propose an Energy Aware Routing Protocol (EARP) which considers node energy in route searching process and chooses nodes with higher energy levels. The EARP aim is to establish the shortest route from source to destination that contains energy efficient nodes. The performance of EARP is evaluated in terms of packet delivery ratio, network lifetime, end-to-end delay and throughput. Results of simulation done by using NS2 network simulator shows that EARP can achieve both high throughput and delivery ratio, whereas increase network lifetime and decreases end-to-end delay.

This paper presents an. application, of functional analysis to the minimal∗time control problem where two or more energy constrained systems of the same order must be taken to an initially unknown common point in state space. Two different types of problems with different forms of the energy constraint are described in detail. The development depends on the fact that the reachable region of a system with an energy constraint on the input vector is an ellipsoid.