Predicting battery depletion of neighboring wireless sensor nodes

With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy inference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

Simulation environment for maritime safety and security systems

Efficient monitoring and management of maritime activities is a critical task for all coastal states. As maritime traffic increases so too do the risk and impact of accidents, pollution and criminal activities. The goal of the "Simulation Environment for Maritime Safety and Security Systems" project to design and implement a Simulation Framework that is capable of producing realistic simulations of a collection of sensors observing different possible maritime events such as collisions, criminal activities. In order to achieve these goals following technical objectives were realized a.) Fusing different Automatic Identification Sys-tem (AIS) datasets, b.) Simulate different sensor of different coverage and c.) Collect data related to vessel from various websites. d.) Generate System Tracks. The System Tracks describe different sensors observing sequences of events and are expressed in the NIEM XML format. The Simulation Framework simulates the sensors of different coverage using the concept of "Sensoring"- a technique used to filter the large maritime area based on the range of the sensor. The Simulation Framework that we have conceived in this project would be helpful for testing and simulating different sensors types and coverage and train monitoring personnel

Simulation environment for maritime safety and security systems

Efficient monitoring and management of maritime activities is a critical task for all coastal states. As maritime traffic increases so too do the risk and impact of accidents, pollution and criminal activities. The goal of the "Simulation Environment for Maritime Safety and Security Systems" project to design and implement a Simulation Framework that is capable of producing realistic simulations of a collection of sensors observing different possible maritime events such as collisions, criminal activities. In order to achieve these goals following technical objectives were realized a.) Fusing different Automatic Identification Sys-tem (AIS) datasets, b.) Simulate different sensor of different coverage and c.) Collect data related to vessel from various websites. d.) Generate System Tracks. The System Tracks describe different sensors observing sequences of events and are expressed in the NIEM XML format. The Simulation Framework simulates the sensors of different coverage using the concept of "Sensoring"- a technique used to filter the large maritime area based on the range of the sensor. The Simulation Framework that we have conceived in this project would be helpful for testing and simulating different sensors types and coverage and train monitoring personnel

Design of mechanical assists for medium girder bridge

U.S. Army bridge crewmembers perform tough manual materials handling (MMH) tasks during assembly of a Medium Girder Bridge (MGB); designed for rapid manual assembly under adverse conditions. The bridge components are extremely heavy and are designed to be carried and assembled using simple hand tools. A human factors assessment of the MMH tasks revealed that the bridge crewmembers undergo high biomechanical stresses which lead to various musculoskeletal disorders. This study deals with the application of Human Factors Engineering and Mechanical Engineering methodologies to design and build mechanical assists which would reduce injury caused by manual material handling of bridge components and at the same time improve the efficiency of the bridge building process --Abstract, page iii

Assessment of proactive transmission power control for wireless sensor networks

In order to prolong lifetime of Wireless Sensor Networks (WSN), Transmission Power Control (TPC) techniques are employed. The existing TPC schemes adjust the transmission power mostly reacting to changes at link quality between communicating nodes. Proactive TPC has been proposed in the recent past as reactivity does not address the need for reliability. Efficiency of a proactive TPC is determined by its prediction accuracy to link quality, ease of configuration and energy efficiency. Current state-of-the-art does not argue about proactive TPC methods on those requirements. This paper provides a targeted analysis of four prominent algorithms such as Discrete Kalman Filter (DKF), Exponentially Weighted Moving Average (EWMA), Simple Moving Average (SMA), Weighted Moving Average (WMA), and Linear Regression (LR) that could be employed in a proactive low-power TPC technique. Our experiments indicate that prediction accuracy of DKF has the least forecasting error and outperforms the prediction accuracy of all other algorithms under discussion. Amongst the Moving Average algorithms, the prediction accuracy of WMA is significantly better and linear regression algorithm has the worst performance. Evaluating the cost involved in terms of radio power and ease of configuration, WMA is the best algorithm for implementing proactive TPC

Reliable low-power wireless networks over unstable transmission power

Internet of Things promises large scale interconnected sensing and actuation capabilities in domains, areas, applications and activities never accessed before by Internet. Besides other technical barriers, wireless network node lifetime impedes its applicability. To reduce the energy cost incurred by wireless communication, several existing mechanisms typically downscale the power of transmitters. However, this increases the instability of the link and aggravates the hidden-node terminal and energy-hole problems. In this work, we assess the effect of transmission power on the performance of a low-power wireless network. Both MAC and routing signaling are taken into account to estimate a more realistic impact of power scaling in the energy efficiency of a network. Our experiments in various settings demonstrate a high cost of low transmission power in terms of both duty cycle and collisions. Symmetrically, high transmission power ensures higher PDR and energy efficiency in a network with multiple source nodes and CCA enabled

Data Driven Transmission Power Control for Wireless Sensor Networks

Transmission Power Control (TPC) is employed in the sensor nodes with the main objective of minimizing transmission power consumption. However, major drawbacks with well-known TPC are time consuming and energy inefficient initialization phase. Moreover, they employ Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI) metrics for initialization phase that are sensitive to environmental conditions and hence are not appropriate parameters to adjust the power. To overcome these shortcomings of existing TPC, we propose a novel TPC algorithm dubbed as Data-Driven Transmission Power Control (DA-TPC) that has shorter initialization phase and uses priority of data as the only metric to adjust the power level. The two main aims of this paper are to minimize power consumption during initialization phase and to show how by utilizing priority of data as a sole metric for power adaptation improves reliability and decreases not only latency but also overall energy consumption while transmitting data packets

Predicting battery depletion of neighboring wireless sensor nodes

With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy inference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

An illustrative application example: cargo state monitoring

This chapter describes a real self-adaptive system carried out using the DEMANES tool chain. This chapter focuses on the design and implementation stages of a real use case development. The use case\u3cbr/\u3eunder study is a subsystem, called Cargo Monitoring System (CMS) , that monitors the state of the container cargo and push all the data to a back office infrastructure for further processing. The containers can be on a truck, a train, or any other appropriate transportation means, or stacked in a container terminal or a cargo ship. A WSN will measure physical magnitudes (temperature, humidity and so on) inside a container and will forward data to others processing nodes in the CMS network. The CMS has to meet several self-adaptive requirements. For instance, the parameters of the CMS elements that monitor the container cargo state are reconfigured accordingly to adapt to internal or external changes (e.g. a low battery level or a container temperature out\u3cbr/\u3eof the adequate bounds), and the CMS adapts the WSN nodes power transmission to save energy while providing an acceptable quality of service