Residual Battery Level Research Articles

DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance- and energy-constrained ${k}$ -means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on ${k}$ -means clustering by 42%.

Lifetime Estimation and Measurement for Wireless Ad Hoc Networks

Mobile ad-hoc networks (MANET) is a popular choice for “wireless communication network” due to ease of deployment. Nodes in MANET are battery operated, movable, and compact. They can sense, manipulate and communicate data wirelessly. Limited battery power of the nodes is one of the major constraints of MANET. This paper proposes a network lifetime model that considers residual energy and actual discharge rate of the battery along with the energy consumption in different modes like transmit, receive, sleep, idle, active and processing while calculating the lifetime. A circuit implementation of node with Arduino Mega 2560, ZigBee transceiver, 2100 mAh NiMH rechargeable battery was done to compare lifetime with conventional dynamic source routing (DSR) and modified Least Max Dynamic Source Routing (LMDSR) algorithms. The DSR algorithm always selects the shortest path between source and destination nodes. But the LMDSR algorithm also considers the residual battery levels of the nodes to avoid overuse of the node(s) with low battery. This will prevent the early exhaustion of node(s) which may be the reason for reduced network lifetime. The result analysis shows that the implementation of LMDSR algorithm improves the network lifetime on an average by 31% and reduces the energy consumption by 21% with a slight decrease in throughput.

Battery-Wear-Model-Based Energy Trading in Electric Vehicles: A Naive Auction Model and a Market Analysis

This paper proposes auction-based energy trading among electric vehicles (EVs) with consideration of practical battery status. At an arbitrary time, each EV can be a seller or a buyer according to their residual battery level and required energy for reaching the predefined destination. Under energy trading among EVs, there is an energy pool managed by an auctioneer in the market that gathers surplus energy from sellers and supplies it to buyers using an auction mechanism. Each buyer individually decides the initial bidding price and the amount of energy to buy from sellers according to an expected cost savings compared with buying from macrogrids as well as its battery wear-out cost and charging/discharging efficiency. Similarly, each seller also individually decides its initial selling price and the amount of energy for sale subject to a tradeoff between the received revenue and discharging cost depending on its battery status. Notably, we provide a battery status model of EVs for designing well-defined utilities of both sellers and buyers. We design a naive auction process such that, in the auction process, the auctioneer controls the bidding increment to determine the best prices on a set of energies offered to multiple buyers through an iterative procedure. Finally, we show that distributing the energy based on a well-defined utility function converges to a unique optimal distribution for maximizing the payoff of all participating EVs.

Energy efficient routing in wireless sensor networks via circulating operator packets

Routing protocols in wireless sensor networks are critical as they affect performance, lifetime and scaling. A novel approach to collect data in sensor networks is presented in this paper. The end-user can configure devices to operate for a desired lifetime and achieve reliable operation for that duration—this is the motivation behind the development of this protocol. In the proposed protocol, data routing is controlled by a special packet—the Operator Packet. The Operator Packet circulates through the network (a) allowing nodes to report data when they need to and (b) is circulated such that it preferentially uses nodes with more data and with higher residual battery levels. Time complexity analysis of the the heuristic approach presented in this paper for the circulation algorithm takes O(n) time. The algorithm creates an opportunistic routing condition—the nodes operate only if their battery levels permit. This leads to controllable lifetime, with average lifetimes achieved by the nodes being as high as 99% of the preconfigured targets. If nodes in the network are configured with similar target lifetimes, a highly synchronised network death is possible. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and Hybrid Energy Efficient Distributed clustering protocol (HEED), the proposed protocol achieves higher lifetimes (60 and 165% respectively) and improved Coefficient of Synchronous Death (17 and 102% respectively). A corrective method to achieve reduced data drops in deployments is also suggested and analysed.

Extending Video Playback Time With Limited Residual Battery

In this letter, the performance of battery-aware-based rate adaptation for video streaming on mobile devices is evaluated. Complementary to conventional channel-aware-based rate adaptation, the proposed scheme jointly adapts, to address the effects of time-varying wireless network conditions as well as, the residual battery on mobile devices. Numerical analysis and simulations conducted under various network conditions and residual battery levels show the potential of the proposed adaptation scheme to extend video playback time by 40% in comparison with the conventional adaptation scheme.

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

Multi-Metric Clustering in Mobile Ad-Hoc Networks using Firefly Optimization Algorithm

Mobile Ad-Hoc Network (MANET) is a form of a wireless network where the devices are dynamic and able to configure themselves on the fly. In such an environment, for supporting energy efficient and robust communication, it is expected that cluster based routing is a predominant solution. The cluster based routing divide the entire network into varying size groups known as clusters. While grouping nodes into clusters, it is essential to choose efficient clusterheads(CHs) based on their potential. One among the good metrics is that each node's associativity which can configure the mobility of a node in relation to its neighbor. The proposed work also gives importance to residual battery level and degree(number of neighborhood nodes) of each node to select the CHs. Finding optimal clusters in case of dynamic MANET is a type of NP-Hard problem which can get a solution by using various evolutionary algorithms, swarm optimization techniques, etc. One of the recent methods, Firefly optimization algorithm is applied in the proposed work. This optimization method will try to find out stable CHs with multiple objectives which are used in framing the fitness function. The different objectives considered in the proposed work will lead to the benefit that the proposed clustering algorithm suitable for both static and dynamic environment and also for the environment with a different battery capacity of the nodes. Evaluating against the genetic algorithm (GA) and particle swarm optimization (PSO) techniques, the proposed firefly algorithm shows better performance under different simulation scenarios.

Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks

In this paper, a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for Mobile Ad-hoc NETworks (MANETs) is presented. In DFES-AODV route discovery phase, each node uses a Mamdani fuzzy logic system (FLS) to decide its Route REQuests (RREQs) forwarding probability. The FLS inputs are residual battery level and energy drain rate of mobile node. Unlike previous related-works, membership function of residual energy input is made dynamic. Also, a zero-order Takagi Sugeno FLS with the same inputs is used as a means of generalization for state-space in SARSA-AODV a reinforcement learning based energy-aware routing protocol. The simulation study confirms that using a dynamic fuzzy system ensures more energy efficiency in comparison to its static counterpart. Moreover, DFES-AODV exhibits similar performance to SARSA-AODV and its fuzzy extension FSARSA-AODV. Therefore, the use of dynamic fuzzy logic for adaptive routing in MANETs is recommended.