Investigations on Energy Efficiency for WSN Routing Protocols for Realistic Radio Models

In this paper, we have extended the Prowler simulator by integrating different realistic radio models into it and comparatively analyzed the effect of the channel behavior on the network layer specifically the WSN routing protocols. The simulation results indicate that the CF protocol consumes the highest energy amongst all the protocols in case of RMGMF while RTS protocol has the highest energy efficiency in case of NRM. Thus it has been concluded that the RTS protocol can be applied to achieve energy efficient routing in wireless sensor networks

Operator calculus approach for route optimizing and enhancing wireless sensor network

Route optimization is one of important feature in wireless sensor networks in order to enhancing the life time of WSNs. Since Centrality is one of the greatest challenges in computing and estimating the important node metrics of a structural graph, it is necessary to calculate and determine the importance of a node in a network. This paper proposes an alternative way to optimizing the route problems which is based on multi-constrained optimal path (MCOP) and operator calculus approach. A novel routing protocol called Path Operator Calculus Centrality (POCC) is proposed as a new method to determine the main path which contains high centrality nodes in a wireless sensor network deployment. The estimation of centrality is using the operator calculus approach based on network topology which provides optimal paths for each source node to base station. Some constraints such as energy level and bit error rate (BER) of node are considered to define the path centrality in this approach. The simulation evaluation shows improved performance in terms of energy consumption and network lifetime

Strengths and Weaknesses of Prominent Data Dissemination Techniques in Wireless Sensor Networks

Data dissemination is the most significant task in a Wireless Sensor Network (WSN). From the bootstrapping stage to the full functioning stage, a WSN must disseminate data in various patterns like from the sink to node, from node to sink, from node to node, or the like. This is what a WSN is deployed for. Hence, this issue comes with various data routing models and often there are different types of network settings that influence the way of data collection and/or distribution. Considering the importance of this issue, in this paper, we present a survey on various prominent data dissemination techniques in such network. Our classification of the existing works is based on two main parameters: the number of sink (single or multiple) and the nature of its movement (static or mobile). Under these categories, we have analyzed various previous works for their relative strengths and weaknesses. A comparison is also made based on the operational methods of various data dissemination schemes

Reliable & Efficient Data Centric Storage for Data Management in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become a mature technology aimed at performing environmental monitoring and data collection. Nonetheless, harnessing the power of a WSN presents a number of research challenges. WSN application developers have to deal both with the business logic of the application and with WSN's issues, such as those related to networking (routing), storage, and transport. A middleware can cope with this emerging complexity, and can provide the necessary abstractions for the definition, creation and maintenance of applications. The final goal of most WSN applications is to gather data from the environment, and to transport such data to the user applications, that usually resides outside the WSN. Techniques for data collection can be based on external storage, local storage and in-network storage. External storage sends data to the sink (a centralized data collector that provides data to the users through other networks) as soon as they are collected. This paradigm implies the continuous presence of a sink in the WSN, and data can hardly be pre-processed before sent to the sink. Moreover, these transport mechanisms create an hotspot on the sensors around the sink. Local storage stores data on a set of sensors that depends on the identity of the sensor collecting them, and implies that requests for data must be broadcast to all the sensors, since the sink can hardly know in advance the identity of the sensors that collected the data the sink is interested in. In-network storage and in particular Data Centric Storage (DCS) stores data on a set of sensors that depend on a meta-datum describing the data. DCS is a paradigm that is promising for Data Management in WSNs, since it addresses the problem of scalability (DCS employs unicast communications to manage WSNs), allows in-network data preprocessing and can mitigate hot-spots insurgence. This thesis studies the use of DCS for Data Management in middleware for WSNs. Since WSNs can feature different paradigms for data routing (geographical routing and more traditional tree routing), this thesis introduces two different DCS protocols for these two different kinds of WNSs. Q-NiGHT is based on geographical routing and it can manage the quantity of resources that are assigned to the storage of different meta-data, and implements a load balance for the data storage over the sensors in the WSN. Z-DaSt is built on top of ZigBee networks, and exploits the standard ZigBee mechanisms to harness the power of ZigBee routing protocol and network formation mechanisms. Dependability is another issue that was subject to research work. Most current approaches employ replication as the mean to ensure data availability. A possible enhancement is the use of erasure coding to improve the persistence of data while saving on memory usage on the sensors. Finally, erasure coding was applied also to gossiping algorithms, to realize an efficient data management. The technique is compared to the state-of-the-art to identify the benefits it can provide to data collection algorithms and to data availability techniques

Reinforcement Learning Framework for the self-learning Suppression of Clutch Judder in automotive Drive Trains

In electromechanically actuated clutches, the active damping of vibrations by means of control of the clamping force allow the use of high performance materials in the friction pairing, which makes a more energy and cost efficient design of the clutch. In this work, a reinforcement learning framework for the control of the clamping force for the active suppression of judder vibrations is proposed and developed

A Learning-based Adaptive Routing Tree for Wireless Sensor Networks

Abstract — One of the most common communication patterns in sensor networks is routing data to a base station, while the base station can be either static or mobile. Even in static cases, a static spanning tree may not survive for a long time due to failures of sensor nodes. In this paper, we present an adaptive spanning tree routing mechanism, using real-time reinforcement learning strategies. We demonstrate via simulation that without additional control packets for tree maintenance, adaptive spanning trees can maintain the “best ” connectivity to the base station, in spite of node failures or mobility of the base station. And by using a general constraint-based routing specification, one can apply the same strategy to achieve load balancing and to control network congestion effectively in real time. Index Terms — constraint-based routing, real-time reinforcement learning, routing tree, wireless sensor networks I