Design and implementation of a communicating method for WSN

The sensor nodes present in the wireless sensor networks are constrained of energy as they are powered with the help of battery. Deployment of the sensor nodes in the hostile environment makes it unfavorable for the people to change the battery of the senor nodes when it is expired. Due to the energy limitations there is a great need of providing any energy efficient way of communication for the wireless sensor networks. Several techniques of offering communications in a sensor network use the classical layered method that results in great overhead of the network and high energy consumption. It will be very better when a unified technique is present for converting the functions of common protocol to the cross layer method. A cross layer protocol is been implemented in this project to provide congestion control, better routing over the cross layers. This cross layer protocol is designed based on the initiative determination present in cross layer module. This method offers congestion control forwarding based on initiatives contention based on receivers and better communication between the sensor nodes of a wireless sensor network. The implementation of this initiative determination is very easy as it just involves the comparison with the threshold values. Through this cross layer protocol the functions of each layer can be combined very easily .The performance of this cross layer protocol is also identified in this project. Through this cross layer protocol better communications can be provided between the sensor nodes of a wireless sensor networks and also is far better than the classic layered protocols with respect to the energy consumption and network performance

Conserving energy through neural prediction of sensed data

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points mak- ing a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energ

Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources

We present a novel dynamic duty cycling scheme to maintain stochastic consistency for caches in sensor networks. To reduce transmissions, base stations often maintain caches for erratically changing sensor sources. Stochastic consistency guarantees the cache-source deviation is within a pre-specified bound with a certain confidence level. We model the erratic sources as Brownian motions, and adaptively {\it predict} the next cache update time based on the model. By piggybacking the next update time in each regular data packet, we can dynamically adjust the relaying nodes' duty cycles so that they are awake before the next update message arrives, and are sleeping otherwise. Through simulations, we show that our approach can achieve very high source-cache fidelity with low power consumption on many real-life sensor data. On average, our approach consumes 4-5 times less power than GAF~\cite{gaf}, and achieves 50\% longer network lifetime

Applications of Prediction Approaches in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes

Developing Function Blocks for Collecting Data and Integrating Legacy Systems in Manufacturing and Logistics

Manufacturing enterprises have been increasingly technology-driven during recent decades. Industry 4.0 promotes smart manufacturing and intelligent systems which can seamlessly communicate with each other and enable decentralized decision-making by monitoring the factory-floor process. This calls for the Information Communication Technologies (ICT) infrastructure to be effectively incorporated with the industries. Industry 4.0 presents the concept of “smart factory” in which Cyber-Physical-Systems (CPS) fuses the physical systems with the Internet of Things (IoT), enabling higher levels of interoperability and Information transparency. However, manufacturing enterprises in the recent past have characterized their efficiency by how prominently and adequately they adopt and utilize their IT solutions and how feasible those solutions are to integrate with their legacy systems. Enterprise Integration, in particular, has become more challenging owing to the highly dynamic manufacturing environment. System integration has become an indispensable field to be addressed , especially when the industry adopts connected enterprise paradigm. Connected enterprise systems enable industries to leverage their technologies to collect, analyze and refine their data to help them make better business decisions. In a recent trend, IT systems in manufacturing are majorly driven towards the cloud and collaborative solutions as a result of the exponential growth of internet technologies and their ability to adapt to rapid changes in the market. Collaborative frameworks are widely preferred by the enterprises as they enable better communication, increases productivity and improve business execution. They are critical for a business to function with agility in this fast pacing and changing world. One such platform is provided by the Cloud Collaborative Manufacturing Networks (C2NET) project that optimizes the supply network of manufacturing and logistics assets. This thesis research proposes an approach to integrate heterogeneous legacy systems by showcasing an implementation which favors robust data collection. This implementation is made possible by adopting Production Logistics and Sustainability Cockpit (PLANTCockpit) Open Source solution, which functions as a viable interface for real-time data collection and data-logistics thus enhancing the process optimization of the manufacturing enterprise. PLANTCockpit OS is a modular solution which enables to build and deploy flexible loosely coupled entities known as Function Blocks (FBs) that facilitate seamless legacy system integration and robust information exchange between the systems. This thesis also fulfills the C2NET project requirement to define the possibility of effective integration of PLANTCockpit OS in the C2NET reference architecture

Energy Efficient Data Collection in Distributed Sensor Environments

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into a sensor database that resides at (more powerful) servers. A natural tradeoff exists between the sensor resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. This paper proposes adaptive data collection protocols for sensor environments that adjusts to these variations while at the same time optimizing the energy consumption of sensors. Our experimental results show significant energy savings compared to the naive approach to data collection