Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

In this paper, we have investigated on virtual multiple-input-multiple-output (V-MIMO) in wireless sensor networks (WSNs) and carried out the performance analysis for this system. An energy consumption model is developed for a cooperative virtual MIMO WSN. For a given geographical area and number of nodes, we optimized the values of various performance parameters like, the number of cooperative nodes, number of cluster heads, communication range distance and so on, to ensure increased life time of sensor nodes. Simulation results are presented for different cases and scenarios. It is observed that V-MIMO WSN can be efficiently designed and optimized for the targeted applications.

A UAV-assisted CH election framework for secure data collection in wireless sensor networks

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

The selection of wireless sensor networks (WSNs) communication topology and the application of unmanned aerial vehicles (UAV) in data collection are studied in the scenario of being far away from the base station. The topology consists of a set of cluster head nodes that communicate with the UAV. After considering the network energy consumption factors, the optimal number of cluster heads of the network is derived, the network is clustered and the cluster head node is selected. Finally, the cruising path of the drone to collect data from the cluster head is optimized. Simulation results show that the proposed algorithm can effectively save the energy of nodes, prolong network life, and reduce the total distance of the UAV collects data.

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.

We study a novel framework to realize efficient data collection from wireless sensor networks, where an unmanned aerial vehicle (UAV) is dispatched to collect the aggregated data from cluster heads and an unmanned ground vehicle carrying backup batteries moves along with the UAV to compensate for the shortage of UAV energy. Our optimization task is to minimize the mission time of a complete round of data collection, which is formulated as a coordinated traveling salesman problem with battery constraints and is solved by a heuristic path planning algorithm. Numerical results show that our proposal outperforms compared methods under appropriate configuration.

In this paper, the optimization strategies of routing protocols are analysed with respect to energy utilization of sensor nodes in Wireless Sensor Networks (WSNs). Routing Protocols are in charge of discovering and maintaining the routes in the network. Different routing mechanisms have been proposed to address energy optimization problem in sensor nodes. Clustering mechanism is one of the most efficient mechanisms which cater to the requirements of energy conservation in wireless sensor networks. To check the efficiency of different clustering scheme against modelled constraints, we select five cluster based routing protocols; Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC), and Hybrid Energy Efficient Distributed protocol. To validate our mathematical framework, we perform analytical simulations in MATLAB by choosing number of alive nodes, number of dead nodes, number of packets, number of cluster heads, as performance metrics.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

In this paper a new method, Optimal Path from Source to Sink Node (OPSSN), is proposed for achieving the Quality of Service (QoS) in terms of packet delivery reliability and better energy consumption in Wireless Sensor Networks (WSN). Routing in WSN has more expected node failures than the traditional networks; so to establish a routing protocol which supports QoS in WSN, requires considering the reasons for node failure to improve network performance. In case of failure (node failure or link failure), this method will offer automatic adaptation to dynamic linking and low processing overhead by choosing the default path through intensive nodes. This method increases the number of spare neighbors for original nodes which are preserved for data transmission through them, to be used in case of failure. This algorithm is simulated using MATLAB. Results showed that OPSSN achieved better performance compared to LEACH in terms of network life time, packets received at Base Station (BS), number of Cluster Heads (CH) per round, and total energy consumption.

A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensor nodes that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, fire, and pressure. Many problems, including data transmission, power consumption and selecting cluster heads, may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. Most of the proposed protocols are based on the Genetic Algorithm as an optimization technique to select the Cluster Heads (CHs) or to find a multi-hop path for sending the data from the CHs to the Base Station (BS). This paper presents a comprehensive study of the protocols for WSNs that are proposed to come up with these issues. This study emphasises on CHs selection protocols and multi-hop path finding protocols and their strengths and weaknesses. A new taxonomy is presented to discuss these protocols on the basis of different classes. A complete comparison of the main features and behaviors of the protocols is conducted. This study will give basic guidelines for the researchers those have a motivation to develop a new CHs selection protocol or a multi-hop path finding protocol.

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

In general, wireless sensor network works by a small battery-powered, or limited energy. Once the wireless sensor network is deployed, the energy of small sensor nodes can not be replaced. So, to improve energy efficiency and extend the survival time of the whole network is a crucial issue. This paper presents a new type of energy balancing algorithm for wireless sensor networks. The BCDCP-M algorithm draws on the main idea of the BCDCP routing protocol. When the base station divides the network, the new algorithm makes the number of cluster heads equal to the optimal number of cluster heads as far as possible. On cluster head election, not only the average energy of the sensor network, but also the remaining energy of the individual node must be taken into account. In data transmission, we use the multi-hop method to select the optimal path. The simulation results show that the survival time of the network in the new BCDCP-M algorithm is 19% longer than BCDCP.