Mobile charging and data gathering in multiple sink wireless sensor networks: how and why

Recently, the problem of using efficient the number of mobile devices starting from multi-sink to go to charge and collect data of sensors such that sensors can work forever has received a great deal of attention in Wireless Rechargeable Sensor Network (WRSN). Many methods have been proposed for the WRSN systems such that mobile device can charge and collect data from sensors. However, most of previous works often require lots of mobile devices while the cost of mobile device is very high. In this paper, we investigate the Periodic Energy Replenishment and Data Collection with multiple sink (PERDCMS) problem and propose a new algorithm, called the Mobile Device Scheduling Algorithm (MDSA), to using limited number of mobile devices for charging and collecting data for sensors. Simulation results show that the MDSA has better performance than other method

Data Aggregation Scheme Using Multiple Mobile Agents in Wireless Sensor Network

Wireless sensor networks (WSNs) consist of large number of sensor nodes densely deployed in monitoring area with sensing, wireless communications and computing capabilities. In recent times, wireless sensor networks have used the concept of mobile agent for reducing energy consumption and for effective data collection. The fundamental functionality of WSN is to collect and return data from the sensor nodes. Data aggregation’s main goal is to gather and aggregate data in an efficient manner. In data gathering, finding the optimal itinerary planning for the mobile agent is an important step. However, a single mobile agent itinerary planning approach suffers from two drawbacks, task delay and large size of the mobile agent as the scale of the network is expanded. To overcome these drawbacks, this research work proposes: (i) an efficient data aggregation scheme in wireless sensor network that uses multiple mobile agents for aggregating data and transferring it to the sink based on itinerary planning and (ii) an attack detection using TS fuzzy model on multi-mobile agent-based data aggregation scheme is shortly named as MDTSF model

Unmanned ground vehicle system to collect soil moisture data

With an increased interest in precision agriculture, it is important to identify efficient ways to monitor soil moisture. Soil moisture can be monitored using handheld sensors, but this method is laborious and time consuming. Remote methods, such as radar systems can be used as well, but these methods require ground truth data to verify their accuracy. It becomes clear that to collect this data regularly and reliably, a mobile robotic device is necessary. This thesis proposes to implement mobile robot take soil moisture measurements with less human effort than existing methods while maintaining the same accuracy. This soil moisture data collection system uses an unmanned ground vehicle (UGV) to take measurements with position data. This system uses an actuator inserted soil moisture probe, and a radio frequency identification (RFID) sensing system that uses buried moisture sensing tags. Field testing of both measurement systems showed that the actuator-based system worked reliably

Ferry–Based Directional Forwarding Mechanism for Improved Network Life-Time in Cluster-Based Wireless Sensor Network

Considerable energy saving can be achieved with mobility-based wireless sensor networks (WSN's), where a mobile node (ferry) visits sensing nodes in a network to collect sensed data. However, the critical issues of such WSN's are limited networks lifetime and high data latency, these critical issues are due to the slow mobility and relatively long route distance for ferries to collect and forward data to the sink. Incorporating ferries in WSNs eliminates the need for multi-hop forwarding of data, and as a result, reduce energy consumption at sensing nodes. In this paper, we introduce the One Hop Cluster-Head Algorithm (OHCH), where a subset of ferries serve as cluster heads (CH), travel between nodes with short distance mobility, collect data originated from sources, and transfer it to the sink with minimum hop count possible, this approach can achieve more balance between network energy saving and data collection delay, also, it is an efficient design to combine between ferries and noise

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations

Improved NOVSF-TM based Addressing and Energy Efficient Routing in ETR Protocol

A small WSN is a collection of micro-sensors. Sensors send or receive data to a sink node, which collect and processes it. The Tree-Routing (TR) protocol was initially designed for such network. TR uses strict parent-child links for data forwarding. Hence, it saves bandwidth and energy by preventing network from flooding path search messages. For a large network TR shows large hop-count and more energy consumption. The Enhanced-Tree-Routing (ETR) protocol implemented over TR has structured node address assignment scheme. It considers other one-hop neighbor links, along with parent-child links, for packet forwarding if, it is found to be the shortest path to sink. Such decision in ETR involves minimum computation energy. Instead ETR, the emerging demand for data intensive and energy2013;efficient applications, needs new or improved routing protocols. In this paper we have proposed Non-Blocking-Orthogonal-Vector Spreading- Factor-Time-Multiplexing (NOVSF-TM) technique for sensor node addressing and Mobile Sinks placement so as to improve ETR protocol. The addressing scheme of NOVSF TM is shorter than ETR. Mobile Sinks positioning, at feasible sites, helps reducing excessive hop-count. This eliminate excessive multi-hoping and save energy. Simulation result shows that NOVSFTM technique is more energy-efficient than ETR protocol

Smart mobility: a mobile approach

The Internet of Things (IoT) is one of the key ingredients for the realization of Smart Cities. IoT devices are essential components of the Smart Cities infrastructure, as they can provide information collected from the environment through sensors or allow other systems to reach out and act on the world through actuators. IoT data collection, however, is not limited to sensors and machines, but to data from social networks, and the web. Social networks have a huge impact on the amount of data being produced daily, becoming an increasingly central and important data source. The exploitation of these data sources, combined with the growing popularity of mobile devices, can lead to the development of better solutions to improve people’s quality of life. This paper discusses how to take advantage of the benefits of mobile devices and the vast range of information sources and services, such as traffic conditions, and narrow, closed or conditioned roads data. The proposed system uses a real-time collection, organization, and transmission of traffic and road conditions data to provide efficient and accurate information to drivers. With the purpose of supporting and improving traffic data collection and distribution, an Android application was developed to collect information about extraordinary events that take place in a city, providing warnings and alternative routes to drivers and helping them to improve their time management. The developed solution also exploits the existing gaps in other applications, implementing a more specific solution for the Madeira Island traffic condition problems.info:eu-repo/semantics/acceptedVersio

Efficient Mobile Data Collection with Mobile Collect

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A M-HEALTH PLATFORM FOR SUPPORTING CLINICAL DATA INTEGRATION AND SERVICE DELIVERY: AN EXAMPLE FROM AUGMENTATIVE AND ALTERNATIVE COMMUNICATION INTERVENTION

Improving the quality of healthcare while simultaneously reducing its overall costs remains a challenge. One of the recommended approaches for achieving this goal is to build high quality data collection and reporting systems to facilitate evidence-based practice (EBP), which emphasizes the importance of using the solid evidence available to make optimal clinical decisions. Along with the rapid adoption of mobile technologies in health care, many clinicians also use smartphones and tablets to collect and integrate clinical data. Verbal communication is an essential skill for human beings and a major factor that influences the overall quality of life. People with communication disabilities (PwCD) may benefit from Augmentative and Alternative Communication (AAC) intervention. However, existing AAC technologies are not currently able to provide efficient real-time and clinically relevant performance measures to speech language pathologists. This work combines mobile and web technologies with AAC intervention to create an integrated platform with a better data analytics approach to support data collection, integration, and reporting, as well as to streamline the workflow of AAC clinical service delivery. To achieve these research goals, three studies were conducted: 1) Exploration; 2) Design & Implementation; and 3) Evaluation. The first study identified the clinical needs and IT requirements of the platform. The second study implemented this platform, including a mobile AAC app, a web-based portal, and data analysis procedures. The last study evaluated the integrated platform and compared the platform to other existing data collection and reporting approaches. The usability and feasibility studies of the mobile AAC app were conducted with able-bodied individuals, health professionals, and PwCD. All participants agreed that the app establishes an alternative treatment protocol for communication rehabilitation, which incorporates AAC intervention with self-learning and real-time monitoring. Overall, the study results confirm that the integrated platform provides the ability to collect comprehensive clinical evidence, automatically analyze collected data in real time, and generate clinically relevant performance measures through an easily accessible web portal. The evaluation concluded that the integrated platform offers a better clinical data analytics approach for AAC clinical service delivery