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The non-convex complementarity constraints present a fundamental computational challenge in energy constrained optimization problems. In this work, we present a new, linear, and robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable. In addition, we bound the worst-case model mismatch and discuss conservativeness. Simulation results further illustrate the effectiveness of this approach.

A multi-agent coverage problem is considered with energy-constrained agents, where a charging station is used to replenish an agent's energy as it becomes depleted while performing the coverage task. The objective of this paper is to compare the coverage performance between centralized and decentralized approaches. To this end, a centralized coverage control method is developed to switch agents between an optimal coverage formation and an optimal charging formation. We design a controller for agent trajectories that include dwell times at the optimal coverage locations and charging times at the charging station to maximize a coverage metric over a finite time interval. Our controller guarantees that at any time there is at most one agent leaving the team for energy repletion. We also derive a tight bound which allows us to quantify the gap between the coverage proposed strategy and the unknown globally optimal coverage performance.

Objective: A new variant of energy-constrained Quickest Path Problem (QPP) is being addressed in Intuitionistic Trapezoidal Fuzzy environment. The consideration of energy with QPP enables to compute the path for continuity aware critical applications. This new variant problem called as Energy-constrained Intuitionistic Trapezoidal Fuzzy Quickest Path Problem (EITFQPP) has been considered and the computations for quickest path have been carried out. Methods: As one of the important feature of the proposed model, the weight parameters associated with a given link, e.g., delay, capacity, energy and data which are completely unknown and therefore, based on the real life situations, they may be assumed as intuitionistic trapezoidal fuzzy numbers. An algorithm to solve the EITFQPP for transmitting the fuzzy data in the network, where the nodes are associated with a sufficient amount of energy (imprecise) for continuous data flow, has been proposed. Results: In order to illustrate the implementation of the proposed algorithm, a numerical example of a benchmark network has been provided. The proposed algorithm successfully provides a quickest path using shortest paths computations under the energy constrained approach. Conclusion: The illustration through a numerical example shows the effectiveness of consideration of energy in the selections of set of paths. Finally, some of the possible directions for the future research are also discussed.

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.

Summary Machine‐to‐machine (M2M) communications being pivotal for internet of things (IoT) networks are characterized by low‐cost, low complexity, and often energy constrained terminals with low tr...

Unmanned aerial vehicles (UAVs) are emerging as promising devices to provide valuable information in rescue applications, which can be dispatched to take photographs for points of interests in disaster areas where humans are hard to approach. Most existing studies focused on the limited energy capacity issue of UAVs when they take photographs, which however ignored an important fact, that is, the photographs taken by the UAVs usually are highly redundant. In this paper we study a novel monitoring quality maximization problem to find a flying tour for an energy-constrained UAV, such that the amount of nonredundant information of the photographs taken by the UAV in its tour is maximized. Due to NP-hardness of the problem, we first propose an approximation algorithm with a quasi-polynomial time complexity. We then devise a fast yet scalable heuristic algorithm for the problem. We finally evaluate the performance of the proposed algorithms via both a real dataset and extensive simulations. Experimental results show that the proposed algorithms are very promising. Especially, the amounts of nonredundant information by the proposed approximation and heuristic algorithms are about 11% and 8% larger than that by the state-of-the-art, respectively. To the best of our knowledge, we are the first to consider the novel problem of collecting nonredundant information with an energy-constrained UAV.

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.

Internet of things is a group of objects or devices that establish communication through Internet. Since, the communication is through Internet, more processing power is required in the devices. The IoT devices are heterogeneous and energy constrained. Owing to constrained processing power, the traditional security procedures are not applicable to be executed in the devices. This generates different attacks like user to root, DoS, remote to local and probe attacks. In the existing systems, the classification is carried out to classify different intrusions by using various machine learning algorithms based on KDD dataset. The results were analyzed with various parameters. ML algorithms led to false classifications with medium accuracy. So, it was unable to classify various attacks perfectly. In this paper, the two variants of recurrent neural network such as long short-term memory and bidirectional LSTM are used for the classification of different IoT attacks. The proposed algorithms reduced the false classification with good accuracy. The parameters are computed for the proposed algorithm and are compared with existing machine learning algorithm.

We study the task of gathering k energy-constrained mobile agents in an undirected edge-weighted graph. Each agent is initially placed on an arbitrary node and has a limited amount of energy, which constrains the distance it can move. Since this may render gathering at a single point impossible, we study three variants of near-gathering:the goal is to move the agents into a configuration that minimizes either (i) the radius of a ball containing all agents, (ii) the maximum distance between any two agents, or (iii) the average distance between the agents. We prove that (i) is polynomial-time solvable, (ii) has a polynomial-time 2-approximation with a matching np-hardness lower bound, while (iii) admits a polynomial-time \(2(1-\tfrac{1}{k})\)-approximation, but no fptas, unless \(\text {p}=\text {np}\). We extend some of our results to additive approximation.keywordsmobile agentspower-aware robotslimited batterygatheringgraph algorithmsapproximationcomputational complexity.

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot)., Comment: typo author's name

Wireless Sensor Networks (WSNs) operate in an energy constrained environment. Prudent approaches that minimize energy usage and consequently maximize network up-time are a subject of much research. Load balancing is a paradigm whose primary aim is to improve network lifetime by balancing the amount of work done by nodes in a network. In this work, we present a novel and simple technique to balance the work done by Cluster Heads (CHs) in clustered WSNs. In most clustered WSNs, CHs take up a majority of the routing and processing work and hence run out of energy sooner. Our proposed approach shares the responsibility of a CH among other non CH nodes in a cluster provided they are comparable to the CH on the CH selection criteria. We built on this idea and using a heuristic approach, built an algorithm that divides workload equally among a CH and some non CHs in a cluster. The selected non CHs are nearly as good as the CH. We tested our implementation on LEACH clustering algorithm. Results show balanced energy utilization and substantial increase in network lifetime.

A multi-agent coverage problem is considered with energy-constrained agents. The objective of this paper is to compare the coverage performance between centralized and decentralized approaches. To this end, a near-optimal centralized coverage control method is developed under energy depletion and repletion constraints. The optimal coverage formation corresponds to the locations of agents where the coverage performance is maximized. The optimal charging formation corresponds to the locations of agents with one agent fixed at the charging station and the remaining agents maximizing the coverage performance. We control the behavior of this cooperative multi-agent system by switching between the optimal coverage formation and the optimal charging formation. Finally, the optimal dwell times at coverage locations, charging time, and agent trajectories are determined so as to maximize coverage over a given time interval. In particular, our controller guarantees that at any time there is at most one agent leaving the team for energy repletion.

We address the problem of achieving persistent surveillance over an environment by using energy-constrained unmanned aerial vehicles (UAVs), which are supported by unmanned ground vehicles (UGVs) serving as mobile charging stations. Specifically, we plan the trajectories of all vehicles and the charging schedule of UAVs to minimize the long-term maximum age, where age is defined as the time between two consecutive visits to regions of interest in a partitioned environment. We introduce a scalable planning strategy based on 1) creating UAV- UGV teams, 2) decomposing the environment into optimal partitions that can be covered by any of the teams in a single fuel cycle, 3) uniformly distributing the teams over a cyclic path traversing those partitions, and 4) having the UAVs in each team cover their current partition and be transported to the next partition while being recharged by the UGV. We show some results related to the safety and performance of the proposed strategy., Accepted to the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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).

This paper is concerned with remote state estimation problem with bandwidth and energy constrained wireless sensor networks(WSNs). To improve the estimation quality under these constrains in addressed system, measurement quantization and event-triggered communication strategies are adopted in WSN. Specifically, quantization strategy and event-triggering mechanism are introduced to describe the set region of original measurements, and a closest ellipsoid approximation of measurement sets method is presented. Subsequently, set-valued Kalman filter based on quantization and event is designed by utilizing the quantizer and trigger information. Finally, an illustrative example is employed to demonstrate the advantages and effectiveness of the proposed methods.

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.

This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-facing sensor footprint, modelled as an anisotropic spherical sector. Coverage and collision avoidance guarantees are met by a hybrid controller consisting of four operating modes: local coverage, global coverage, waypoint scan and subdomain transfer. Energy-aware methods are encoded into the global coverage state to shift the bulk of spatial redistribution onto less constrained agents. Additionally, a novel domain partitioning strategy is used that directs individual agents to explore within concentric hemispherical shells around a centralized charging station. This results in flight paths that are guaranteed to terminate at the charging station in the limit that agent batteries expire. The efficacy of this algorithm is presented through experimental trials with three agents in an indoor environment. Simulations are provided for ten agents.

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.

A novel Doppler shift based technique for measurement of free-swimming fish speed in marine farms using acoustic telemetry tags was developed and evaluated in this study. The proposed method can potentially augment current telemetry systems with a new biologically relevant measurement without significantly changing the size and energy constrained tag-side of the telemetry systems. For speeds in the range of 20 cm s-1-110 cm s-1 an overall relative rms error of less than 10% in measured speed based on the proposed Doppler method was achieved in the tests conducted at a fully stocked commercial fish cage, with an rms error of 7.85 cm s-1 (std. dev. 7.5 cm s-1). The study thus demonstrates the feasibility of measuring the swimming speeds of individual free-ranging fish using this method. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

In recent years drones have been used in many applications such as surveillance, geographic mapping, search and rescue, and weather forecast. Motivated by the increased use of drones in shipping and delivery, in this paper we tackle the problem of parcel delivery by taking into consideration important aspects such as serving on-demand requests, flight duration limitation due to energy constraints, maintaining the safety distance to avoid collisions, and using warehouses as starting and ending points in parcel delivery. In this paper we define the UAS Energy-constrained Delivery Scheduling problem and propose a scheduling mechanism using a multi-source A* algorithm variant. Simulation results show that our algorithm is efficient and scalable with the number of requests and network size.

Simultaneous wireless information and power transfer (SWIPT) can prolong the life of wireless nodes by harvesting energy and decoding information from a same RF signal, which changes the design of energy-constrained wireless network. In multi-hop wireless network, the interference generally exists. Route, interference and SWIPT are dependent. However, the exist works consider SWIPT link resource allocation with given route or only select path for only one flow without interference. Therefore, this paper analyze the influence of interference on SWIPT, and formulate the interference-aware SWIPT routing problem. The simulation results show that it can obtain more performance gains from interference and SWIPT with more flows.

We are concerned with conservative systems $\ddot{q}=\nabla V(q), \; q\in\mathbb{R}^N$ for a general class of potentials $V\in C^1(\mathbb{R}^N)$. Assuming that a given sublevel set $\{V\leq c\}$ splits in the disjoint union of two closed subsets $\mathcal{V}^c_-$ and $\mathcal{V}^c_+$, for some $c\in\mathbb{R}$, we establish the existence of bounded solutions $q_c$ to the above system with energy equal to $-c$ whose trajectories connect $\mathcal{V}^c_-$ and $\mathcal{V}^c_+$. 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 $\nabla V$ on $\partial\mathcal{V}^c_{\pm}$. 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 $(q_c)$., 34 pages, 2 figures, submitted to a journal for publication. KEYWORDS: conservative systems, energy constraints, variational methods, brake orbits, homoclinic orbits, heteroclinic orbits, convergence of solutions

We describe an universal method for quantitative continuity analysis of entropic characteristics of energy-constrained quantum systems and channels. It gives asymptotically tight continuity bounds for basic characteristics of quantum systems of wide class (including multi-mode quantum oscillators) and channels between such systems under the energy constraint. The main application of the proposed method is the advanced version of the uniform finite-dimensional approximation theorem for basic capacities of energy-constrained quantum channels., 33 pages, Section 5 contains the answer to everyone who asked me about the possibility to improve the dimension estimates in arXiv:1707.05641, in v.3 it is shown that the proposed method gives asymptotically tight continuity bounds in more general case

Process, voltage, and temperature (PVT) variations substantially increase the variability of digital CMOS logics and reduce the operation robustness, especially for energy-constrained systems with aggressive voltage scaling. This paper reviews several variation-resilient design techniques for addressing PVT variations to improve the energy efficiency of digital CMOS VLSI circuits. The scope includes static and adaptive design techniques for design-time and run-time optimization, respectively. In addition, an emerging adaptive design strategy combining the fully integrated voltage regulator for system-level optimization is also introduced.

We consider the problem of collectively delivering a package from a specified source to a designated target location in a graph, using multiple mobile agents. Each agent starts from a distinct vertex of the graph, and can move along the edges of the graph carrying the package. However, each agent has limited energy budget allowing it to traverse a path of bounded length b; thus, multiple agents need to collaborate to move the package to its destination. Given the positions of the agents in the graph and their energy budgets, the problem of finding a feasible movement schedule is called the collaborative delivery problem and has been studied before.one of the open questions from previous results is what happens when the delivery must follow a fixed path given in advance. Although this special constraint reduces the search space for feasible solutions, the problem of finding a feasible schedule remains np hard (as the original problem). We consider the optimization version of the problem that asks for the optimal energy budget b per agent which allows for a feasible delivery schedule, given the initial positions of the agents. We show the existence of better approximations for the fixed-path version of the problem (at least for the restricted case of single pickup per agent), compared to the known results for the general version of the problem, thus answering the open question from the previous paper.we provide polynomial time approximation algorithms for both directed and undirected graphs, and establish hardness of approximation for the directed case. Note that the fixed path version of collaborative delivery requires completely different techniques since a single agent may be used multiple times, unlike the general version of collaborative delivery studied before. We show that restricting each agent to a single pickup allows better approximations for fixed path collaborative delivery compared to the original problem. Finally, we provide a polynomial time algorithm for determining a feasible delivery strategy, if any exists, for a given budget b when the number of available agents is bounded by a constant.

We consider a design of distributed detection with energy and bandwidth constraints. A collision-aware distributed detection system with sensors performing truncated sequential probability ratio tests (SPRTs) is proposed to encounter these constraints. The SPRT thresholds are the key parameters controlling the system’s energy consumption and performance. We, then, formulate a constrained optimization problem to find the optimal SPRT thresholds minimizing the system’s detection error probability given an energy allocation.

Biologically plausible mechanism like homeostasis compliments Hebbian learning to allow unsupervised learning in spiking neural networks [1]. In this work, we propose a novel ferroelectric-based quasi-LIF neuron that induces intrinsic homeostasis. We experimentally characterize and perform phase-field simulations to delineate the non-trivial transient polarization relaxation mechanism associated with multi-domain interaction in poly-crystalline ferroelectric, such as Zr doped $\text{HfO}_{2}$ , that underlines the Q-LIF behavior. Network level simulations with the Q-LIF neuron model exhibits a 2.3x reduction in firing rate compared to traditional LIF neuron while maintaining iso-accuracy of 84-85% across varying network sizes. Such an energy-efficient hardware for spiking neuron can enable ultra-low power data processing in energy constrained environments suitable for edge-intelligence.

The performances are investigated in the paper for the decode-and-forward (DF) based simultaneous wireless information and power transfer (SWIPT) system, which are evaluated by the outage probability and ergodic capacity. In the SWIPT system, the source node transmits the signal to the destination node via an energy constrained DF relay node. For utilizing the direct link from the source node to destination node, the destination node adopts the selection combining (SC) scheme to merge the signals coming from different links. The closed-form and integral results for the outage probability and the ergodic capacity of the system are derived, respectively. Simulation results show that the performance of the proposed DF SC scheme is superior to the existing schemes.

In sensors localization, it is not economically argumentative to equip many static nodes with GPS. The most widespread solution is to utilize from mobile location-aware nodes called mobile anchors. A substantial amount of research to propose mobile anchor trajectory to improve localization accuracy, localization latency, network coverage and traversed path has been reported. However, none of the existing static mobile anchor path planning mechanisms has emphasized on increasing mobile anchor lifetime and reliability that is necessary due to limited energy. In this paper we propose a novel mobile anchor trajectory planning scheme called Optimal Priority based Trajectory with Energy Constraint (OPTEC) in order to address these issues. The proposed scheme utilizes the Mixed Integer Linear Programming optimization (MILP) approach for optimal route planning in the presence of location uncertainty for deployed sensors. Several important evaluation metrics including localization coverage, localization success, ineffective beacon points and energy-location uncertainty product are also defined for a comprehensive comparison of static mobile anchor trajectory plans. In this paper, static sensors utilize a range-free localization algorithm. Simulation results reveal that the proposed mobile anchor trajectory planning approach can surpass other existing trajectories in terms of localization error, mobile anchor energy consumption and even sensors lifetime.

One of the most promising technologies of 21st century is the wireless sensor network (WSN). Due to its self-organizing, low cost, low power and sensing capabilities, it has been widely used in many domains such as transportation system, agriculture, medical care, surveillance, Internet of things (IoT) and many more. The use of sensors in IoT is getting bigger attention from researchers and industrialists, as people are using smart devices along with sensors to capture data and get connected round the clock anywhere they want. As these networks are energy constrained, so it is important to use the battery wisely and effectively. The IoT is mainly dependent on sensors and these devices have scare resources. Therefore, it is essential to prolong network lifetime by adopting energy efficient schemes. In this paper, we propose an adaptive energy efficient scheme for energy constrained networks such as WSN and IoT. This work is an extension to our previous work presented in [1]. The objective of this work is to make the scheme adaptive so that the parameters such as grid dimensions, lower bound, upper bound and inter bound gap are set according to the input parameters which are node density and network deployment area. The performance evaluation of the proposed scheme is carried in two ways. One is to check the mean squared error (MSE) by considering merge and split technique and normal distribution (without merge and split). Secondly, extensive experiments were carried out to make the scheme adaptive by considering different parameters and its impact on network lifetime. Results shows that the proposed merge and split technique has minimum MSE as compared to normal distribution (without merge and split) by considering different parameters such as grid dimension, lower bound (LB) and upper bound (UB). Minimum the value of MSE less will be the error thus resulting in better performance. Different values of LB, UB and grid dimension are suggested for input parameters (node density and network deployment area).

As a candidate power supply solution for the Internet of Things, radio frequency (RF) energy harvesting has attracted great attention recently. In this paper, we consider an RF wireless-powered network, which consists of a dedicated power beacon (PB) and multiple user nodes. The PB is assumed to have limited energy and transfers its power to user nodes wirelessly. The user nodes work only based on energy harvested from the PB, and are assumed to be in either energy harvesting mode, energy consumption mode, or idle mode. To maximize the total harvested energy, we coordinate the behaviors of the PB and user nodes, and propose a general charge scheduling scheme that can achieve the system’s maximum harvested energy. We then compare the results of our proposed scheme with the benchmark schemes, and simulation results demonstrate the effectiveness of our proposed scheduling.

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.