MAXIMIZE THE LIFETIME OF SENSOR NETWORK BY LOAD BALANCING USING TREE TOPOLOGY

In many wireless sensor networks due to the limited energy of sensor nodes energy conservation is one of the most important challenges. To enhance the lifetime of the network emphasis is given to design energy efficient routing algorithms. In WSN, sensor nodes which are nearer to the base station having a task of collecting data for the entire area and send to the base station. This node has an additional load and depletes its energy faster. This paper addresses the problem of lifetime maximization by load balancing. This paper proposes energy efficient load balanced data collection algorithm considering different network parameter (e.g., density, degree). In this method, Data collection tree topology is built at the sink node. Performance of the proposed algorithm is evaluated by considering various parameters like topology, availability of resources and the energy utilization of nodes in different paths of the tree, which may vary and ultimately impacts the overall network lifetime. Sensor nodes are switched from their original path to other based on the load and it reduces communication overhead

Autonomous deployment for load balancing k-surface coverage in sensor networks

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Cluster-based Routing Protocols for Energy-Efficiency in Wireless Sensor Networks

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Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Probabilistic approaches to the design of wireless ad hoc and sensor networks

The emerging wireless technologies has made ubiquitous wireless access a reality and enabled wireless systems to support a large variety of applications. Since the wireless self-configuring networks do not require infrastructure and promise greater flexibility and better coverage, wireless ad hoc and sensor networks have been under intensive research. It is believed that wireless ad hoc and sensor networks can become as important as the Internet. Just as the Internet allows access to digital information anywhere, ad hoc and sensor networks will provide remote interaction with the physical world. Dynamics of the object distribution is one of the most important features of the wireless ad hoc and sensor networks. This dissertation deals with several interesting estimation and optimization problems on the dynamical features of ad hoc and sensor networks. Many demands in application, such as reliability, power efficiency and sensor deployment, of wireless ad hoc and sensor network can be improved by mobility estimation and/or prediction. In this dissertation, we study several random mobility models, present a mobility prediction methodology, which relies on the analysis of the moving patterns of the mobile objects. Through estimating the future movement of objects and analyzing the tradeoff between the estimation cost and the quality of reliability, the optimization of tracking interval for sensor networks is presented. Based on the observation on the location and movement of objects, an optimal sensor placement algorithm is proposed by adaptively learn the dynamical object distribution. Moreover, dynamical boundary of mass objects monitored in a sensor network can be estimated based on the unsupervised learning of the distribution density of objects. In order to provide an accurate estimation of mobile objects, we first study several popular mobility models. Based on these models, we present some mobility prediction algorithms accordingly, which are capable of predicting the moving trajectory of objects in the future. In wireless self-configuring networks, an accurate estimation algorithm allows for improving the link reliability, power efficiency, reducing the traffic delay and optimizing the sensor deployment. The effects of estimation accuracy on the reliability and the power consumption have been studied and analyzed. A new methodology is proposed to optimize the reliability and power efficiency by balancing the trade-off between the quality of performance and estimation cost. By estimating and predicting the mass objects\u27 location and movement, the proposed sensor placement algorithm demonstrates a siguificant improvement on the detection of mass objects with nearmaximal detection accuracy. Quantitative analysis on the effects of mobility estimation and prediction on the accuracy of detection by sensor networks can be conducted with recursive EM algorithms. The future work includes the deployment of the proposed concepts and algorithms into real-world ad hoc and sensor networks

Reliable load-balancing routing for resource-constrained wireless sensor networks

Wireless sensor networks (WSNs) are energy and resource constrained. Energy limitations make it advantageous to balance radio transmissions across multiple sensor nodes. Thus, load balanced routing is highly desirable and has motivated a significant volume of research. Multihop sensor network architecture can also provide greater coverage, but requires a highly reliable and adaptive routing scheme to accommodate frequent topology changes. Current reliability-oriented protocols degrade energy efficiency and increase network latency. This thesis develops and evaluates a novel solution to provide energy-efficient routing while enhancing packet delivery reliability. This solution, a reliable load-balancing routing (RLBR), makes four contributions in the area of reliability, resiliency and load balancing in support of the primary objective of network lifetime maximisation. The results are captured using real world testbeds as well as simulations. The first contribution uses sensor node emulation, at the instruction cycle level, to characterise the additional processing and computation overhead required by the routing scheme. The second contribution is based on real world testbeds which comprises two different TinyOS-enabled senor platforms under different scenarios. The third contribution extends and evaluates RLBR using large-scale simulations. It is shown that RLBR consumes less energy while reducing topology repair latency and supports various aggregation weights by redistributing packet relaying loads. It also shows a balanced energy usage and a significant lifetime gain. Finally, the forth contribution is a novel variable transmission power control scheme which is created based on the experience gained from prior practical and simulated studies. This power control scheme operates at the data link layer to dynamically reduce unnecessarily high transmission power while maintaining acceptable link reliability

Game Theoretic Energy Balanced Routing Protocols For Wireless Sensor Networks

A primary concern in the operation of Wireless Sensor Network (WSN) is the issue of balancing energy consumption and lifetime maximization. This dissertation addresses the problem of unbalanced energy consumption in WSNs by designing traffic load balancing geographical routing protocols. In order to provide energy balance; two decentralized, scalable and stable routing protocols are proposed: Game Theoretic Energy Balanced (GTEB) routing protocol for WSNs and three dimensional (3D) Game Theoretic Energy Balance (3D-GTEB) routing protocol for WSNs. GTEB were designed to fit with WSNs deployed in 2D space, while 3D-GTEB designed to work with WSNs deployed in 3D terrain. Both protocols are built based on balancing energy consumption into region level and node level using different game theory in each level. In the first level, evolutionary game theory was used to balance the energy consumption in various packet forwarding sub-regions, while in the second level classical game theory was used to balance the energy consumption in forwarding sub-region nodes. 3D-GTEB benefits from utilizing the third coordinate of nodes\u27 locations to achieve better and accurate routing decision with low network overhead. The protocols where evaluated analytically and experimentally under realistic simulation environment. Thus, the results show not only combining evolutionary and classical game theories are applicable to WSNs, but also they achieve significantly better performance in terms of energy usage, load spreading, and packet delivery ratio under different network scenarios when compared to the state-of-art protocols. Moreover, further investigation is made to evaluate the effectiveness of using game theories by comparing GTEB with three random test protocols. The results demonstrated that the GTEB and 3D-GTEB are prolonged the network lifetime from 33% to 85%, and provided better delivery ratio form 26% to 52% as compared with other three random test protocols and three similar state-of-art routing algorithms

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms