Search

In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network's operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of "communicating concretes," which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions. [ABSTRACT FROM AUTHOR]

In many of the Internet of Things (IoT) applications, a network of sensors that are wirelessly connected is vital because it provides a better picture of remotely sensed environments. However, traditional network data collection consumes a high amount of energy because of data packets that need to be routed on a hop-by-hop basis to the base station. To alleviate this problem, Unmanned Aerial Vehicles (UAV) have been used to travel over the sensed environment to collect data. The time and motion constraints of UAVs mean that they require the shortest and smoothest paths to address these constraints. So, the shortest path is planned based on Travelling Sales Problem (TSP), and to smoothen the path, we used Bezier curves to convert paths that are flyable. The proposed algorithm exhibits faster data collection in comparison with other solutions, while achieving a high delivery rate of packets and a low energy usage. [ABSTRACT FROM AUTHOR]

In multihop scenarios, the sensor nodes nearer to the base station (BS) are overloaded because they handle their own data as well as the information obtained from far away nodes. This induces a higher energy depletion rate in nodes near to the BS causing early death of these nodes resulting in hot spot/energy hole problem in wireless sensor network (WSN). This paper proposes a novel strategy using unequal fixed grid-based cluster along with a mobile data mule for data collection from the cluster head (CH). A CH is selected in such a manner that the cumulative transmission distance for member nodes within the cluster is minimum. The paper has attempted to optimize the values for CH change time or round number (f) and also established a relationship between different size clusters by using a factor (r), as they are playing an important role in the overall performance improvement of the WSN. Integrating a mobile data mule in the protocol enhances its efficiency of handling hot spot problem and makes it more energy effective. Two different WSN-scenarios have been considered based on the movement pattern of the data mule. The results obtained through simulation in both scenarios prove the success of our scheme in terms of energy efficiency, load balancing and network lifetime as compared to the existing protocols. The paper also providing a balance trade-off between delay and high overheads by using a single mule with simple predefined path. It also minimizes the hot spot problem as it sustains more than 3000 rounds, which is far better than the existing methods. [ABSTRACT FROM AUTHOR]

Wireless energy transfer (WET) is a new technology that can be used to charge the batteries of sensor nodes without wires. Although wireless, WET does require a charging station to be brought to within reasonable range of a sensor node so that a good energy transfer efficiency can be achieved. On the other hand, it has been well recognized that data collection with a mobile base station has significant advantages over a static one. Given that a mobile platform is required for WET, a natural approach is to employ the same mobile platform to carry the base station for data collection. In this paper, we study the interesting problem of co-locating a wireless charger (for WET) and a mobile base station on the same mobile platform—the wireless charging vehicle (WCV). The WCV travels along a pre-planned path inside the sensor network. Our goal is to minimize energy consumption of the entire system while ensuring that 1) each sensor node is charged in time so that it will never run out of energy, and 2) all data collected from the sensor nodes are relayed to the mobile base station. We develop a mathematical model for this problem (OPT-t), which is time-dependent. Instead of solving OPT-t directly, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. Our results offer a solution on how to use a single mobile platform to address both WET and data collection in sensor networks. [ABSTRACT FROM AUTHOR]

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate. • The proposed scheme is designed for solar powered WSNs operating with a mobile sink. • The proposed scheme can maximize the utilization of harvested energy at each node. • The proposed scheme can alleviate the hotspot problem significantly. • The proposed scheme can reduce the blackout time for solar-powered sensor nodes. • The proposed scheme can improve the quantity and quality of the collected data. [ABSTRACT FROM AUTHOR]

The energy hole problem is a major concern in the wireless sensor network (WSN) given the uneven energy depletion of the power-constrained sensor nodes. Recently, several studies have incorporated a mobile data collector (MDC) to collect data from sensor nodes where the MDC reaches a set of data collection points (DCPs) to collect data from nearby nodes. This strategy has resolved the underlying cause of unbalanced energy consumption by avoiding the long chain of multi-hop routing. However, in most of the existing protocols, sensor nodes information is relayed to the Base Station (BS) to nominate MDC sojourn points and travel sequence. Such a centralized approach results in a substantial exchange of messages among sensor nodes causing high energy consumption. Moreover, these approaches perceived a uniform data generation rate at sensor nodes while ignoring the limited buffer cache capacity during the MDC path design. In this context, this paper proposes a distributed MDC sojourn point nomination method that significantly improves the message complexity and energy consumption of the WSN. The selected DCPs information is relayed to the BS where a modified Ant Colony Optimization algorithm is applied to construct the MDC traversal path. The performance of the proposed protocol is extensively analysed with some related existing schemes in terms of network parameters like energy efficiency, network lifetime, packet delivery ratio, and end to end delay. Moreover, the scalability of the proposed scheme is evaluated by varying the number of MDC and sensor nodes in the network. • A distributed Mobile Data Collector (MDC) sojourn point nomination method. • MDC data gathering trajectory simultaneously optimizes the energy and cache memory. • Ant Colony Optimization based data collection traversal sequence of the MDC. • Works with uniform as well as non-uniform data generating sources. [ABSTRACT FROM AUTHOR]