Relay selection methods for maximizing the lifetime of wireless sensor networks

Combined analytical and fuzzy techniques are proposed for improving the battery lifetime, performance, as well as energy efficiency of wireless sensor networks (WSNs) with the aid of efficient relay selection methods. We determine the best relay selection method by striking an appealing performance versus network lifetime trade-off. Furthermore, the beneficial regions of cooperation are determined considering asymmetric traffic scenarios, where relaying provides energy saving

Increasing network lifetime by battery-aware master selection in radio networks

Mobile wireless communication systems often need to maximize their network lifetime (defined as the time until the first node runs out of energy). In the broadcast network lifetime problem, all nodes are sending broadcast traffic, and one asks for an assignment of transmit powers to nodes, and for sets of relay nodes so that the network lifetime is maximized. The selection of a relay set consisting of a single node (the ‘master’), can be regarded as a special case of this problem. We provide a mean value analysis of algorithms controlling the selection of a master node with the objective of maximizing the network lifetime. The results show that already for small networks simple algorithms can extend the average network lifetime considerably

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

Energy distribution control in wireless sensor networks through range optimization

A major objective in wireless sensor networks is to find optimum routing strategies for energy efficient use of nodes. Routing decision and transmission power selection are intrinsically connected since the transmission power of a node is adjusted depending on the location of the next hop. In this paper, we propose a location-based routing framework to control the energy distribution in a network where transmission ranges, hence powers, of nodes are determined based on their locations. We show that the proposed framework is sufficiently general to investigate the minimum-energy and maximum-lifetime routing problems. It is shown that via the location based strategy the network lifetime can be improved by 70% and the total energy consumption can be decreased to three-fourths to one-third of the constant transmission range strategy depending on the propagation medium and the size of the network

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

Lifetime Maximization for Amplify-and-Forward Cooperative Networks

[[abstract]]Power allocation strategies are devised to maximize the network lifetime of amplify-and-forward (AF) cooperative networks. We consider the scenario where one source and multiple partners cooperate to transmit messages to the destination. The powers emitted by the users are subject to the SNR requirement at the destination. First, the power allocation strategy that demands the minimum instantaneous aggregate transmit power of all cooperating partners is described and analyzed. The optimal solution results in a form of selective relaying; namely, the user with the best channel condition is selected to help in relaying the message. However, this instantaneous power minimization strategy does not necessarily maximize the lifetime of battery-limited systems. Then, we propose three AF cooperative schemes to exploit the channel state information (CSI), the residual battery energy and the QoS requirement. It is shown that the network lifetime can be extended considerably by taking all these three factors into account.[[fileno]]2030137030021[[department]]電機工程學

An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS