Energy-based Clustering for Wireless Sensor Network Lifetime Optimization

International audienceClustering in wireless sensor networks is an efficient way to structure and organize the network. It aims to identify a subset of nodes within the network and bind it a leader (i.e. cluster-head). This latter becomes in charge of specific additional tasks like gathering data from all nodes in its cluster and sending them by using a longer range communication to a sink. As a consequence, a cluster-head exhausts its battery more quickly than regular nodes. In this paper, we present BLAC, a novel Battery-Level Aware Clustering family of schemes. BLAC considers the battery-level combined with another metric to elect the cluster-head. It comes in four variants. The cluster-head role is taken alternately by each node to balance energy consumption. Due to the local nature of the algorithms, keeping the network stable is easier. BLAC aims to maximize the time with all nodes alive to satisfy application requirements. Simulation results show that BLAC improves the full network lifetime 3-time more than traditional clustering schemes by balancing energy consumption over nodes and still delivering high data percentage

GEAMS: a Greedy Energy-Aware Multipath Stream-based Routing Protocol for WMSNs

Because sensor nodes operate on power limited batteries, sensor functionalities have to be designed carefully. In particular, designing energy-efficient packet forwarding is important to maximize the lifetime of the network and to minimize the power usage at each node. This paper presents a Geographic Energy-Aware Multipath Stream-based (GEAMS) routing protocol for WMSNs. GEAMS routing decisions are made online, at each forwarding node in such a way that there is no need to global topology knowledge and maintenance. GEAMS routing protocol performs load-balancing to minimize energy consumption among nodes using twofold policy: (1) smart greedy forwarding and (2) walking back forwarding. Performances evaluations of GEAMS show that it can maximize the network lifetime and guarantee quality of service for video stream transmission in WMSNs

Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Optimal energy balanced data gathering in wireless sensor networks

Unbalanced energy consumption is an inherent problem in wireless sensor networks where some nodes may be overused and die out early, resulting in a short network lifetime. In this paper, we investigate the problem of balancing energy consumption for data gathering sensor networks. Our key idea is to exploit the tradeoff between hop-by-hop transmission and direct transmission to balance energy dissipation among sensor nodes. By assigning each node a transmission probability which controls the ratio between hop-by-hop transmission and direct transmission, we formulate the energy consumption balancing problem as an optimal transmission probability allocation problem. We discuss this problem for both chain networks and general networks. Moreover, we present the solution to compute the optimal number of sections in terms of maximizing the network lifetime. Numerical results demonstrate that our methods outperform the traditional hop-by-hop and direct transmission schemes and achieve significant lifetime extension especially for dense sensor networks.Haibo Zhang, Hong Shen, Yasuo Ta

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

From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5