Linear Wireless Sensor Networks Research Articles

Linear wireless sensor networks: Classification and applications

Wireless sensor networks (WSNs) constitute a rapidly growing technology, taking advantage of advances in electronic miniaturization that consume less energy for both processing and communication. The cost of these devices is also constantly decreasing, making it possible to use a large number of sensor devices in a wide array of commercial, environmental, military, and health care fields. Many of these applications involve placing the sensors in a linear form, making a special class of these networks which we define as a Linear Sensor Network (LSN). In this paper, the concept of LSNs is expanded, along with a set of applications for which this type of network is appropriate. In addition, motivation for designing specialized protocol is provided that explores linearity of the network to increase the communication efficiency, reliability, fault tolerance, energy savings and network lifetime. Furthermore, classification of LSNs from both topological and hierarchical points of views, is presented and various characteristics, research challenges and underlying opportunities are discussed. Simulation experiments are also presented to compare the performance and reliability of LSNs.

Node Placement Strategies to Prolong Lifetime in One-Dimensional Linear Wireless Sensor Network

We investigate the node distribution strategy in one-dimensional linear wireless sensor networks(WSNs), which aims to avoid the energy hole around the sink. We set up the network model and analyze the energy consumption factors, such as data compression and aggregation, and propose energy balance strategies due to different placement scenarios. In the uniform node distribution scenerio, based on selected optimal hop distance, we can add more backup batteries to some nodes to balance the sensors‘ lifetime. In the hop distance variable scenario, we can select different optimal hop lengths. Simulation results show that, in a WSN with linear topology, aggregation and compression should be supplied with backup battery to balance energy consumptions and to maximize the network lifetime.

An Energy-Balanced Data Gathering Algorithm for Linear Wireless Sensor Networks

Aiming at the data unbalance of sensor nodes in the wireless sensor networks (WSN), this paper mainly studies the data gathering algorithm for linear WSNs. As the data amount varies from the time spent on data gathering of entire network minimal, it is a key factor to balance energy consumption and further prolong the network lifetime. Therefore, this paper proposes a TDMA scheduling algorithm for general k-hop networks, and takes detail performance analysis on the algorithm. Furthermore, we present the method of selecting the optimal hop-count and its formula as well as the formula of the number of timeslots required for converge-cast in order to maximize network lifetime. Finally, we obtain some general conclusions of network optimization based on the theoretical analysis and simulations. Compared with the 1-hop algorithm in (C. Florens and R. McEliece, Packets Distribution Algorithms for Sensor Networks, IEEE INFOCOM, San Diego, pp. 1063–1072, 2003), the TDMA scheduling algorithm for the general k-hop network proposed in our paper is more universal and has more practical application value.

Linear Clustering Algorithm by Competitive Learning Suitable for Coal Mine in Wireless Sensor Networks

Because of the complex environment of coal mines, there are many accidents like communication link fault caused by mobile equipment and sliding stones. Therefore, fast rebuilding of remaining nodes and dynamic adding of nodes toted by the rescuer are required after the disaster. Furthermore, the sensor nodes used for localization of the miner and his equipment can only be deployed redundantly along the straight laneway to form a linear wireless sensor network, which proposes a new requirement of topology control strategy. Combining with the self-organizing learning theory of the artificial neural networks, LCACL is presented, a linear clustering algorithm by competitive learning, in which sensor nodes are divided into mobile nodes which add or leave the network dynamically and fixed nodes which could act as sleep nodes, followers, cluster-bridges, or cluster-heads self-adaptively. On the comprehensive consideration of the remaining energy of the sensor node and the number of neighboring nodes, the competition vector, the base for competition, and evaluation vector, the evaluation standard, are formed, which correspond to the input vector and weight vector of artificial neural networks respectively. A similarity comparison using the Euclidean distance is made between them after normalization, and the competitive selection of roles and conversion are achieved using the Euclidean distance. Only the winner node will be entitled to make a learning adjustment, and then broadcasts the adjusted competition vector, which is the evaluation vector in the next competition. According to the rule of cluster-head selection from odd hops and cluster-bridge selection from even hops, the network is initialized as that nodes are divided into clusters of scale-uniform and low overlap ratio. The cluster-head initiates the competitive selection of the sleep node if there are too many followers, and its followers compete to sleep. Along with the energy consumption of the node, the cluster-head or cluster-bridge initiates a new election if it detects its energy consumption exceeding a certain threshold, and all the nodes in the cluster participate in competition by means of competitive learning to achieve role conversion. The simulation results show that the network organized by LCACL has balanced energy consumption, stronger ability to resist destruction, better expansibility, and longer lifetime of network, and the ratio of overlap could be 14% if the communication radius is 70 meters.

Characterizing the traffic distribution in linear wireless sensor networks

In a multi-hop wireless sensor network (WSN), the traffic load is not evenly distributed over the nodes. For example, the sensors which are one hop away from the sink relay the whole network traffic. This imbalanced traffic distribution can degrade the network lifetime and functionality. Here, an analysis is proposed to characterize the traffic load distribution over a randomly deployed linear WSN. The effect of the number of nodes and their distribution over the network is taken into account and the results are verified through computer simulation.

INVESTIGATING A WIRELESS SENSOR NETWORK OPTIMAL LIFETIME SOLUTION FOR LINEAR TOPOLOGIES

We investigate a known optimal lifetime solution for a linear wireless sensor network through simulation, and propose alternative solutions where a known optimal solution does not exist. The network is heterogeneous in the sensors' energy distribution and also in the amount of data each sensor must communicate. As a basis for comparison, we analyze the lifetime of a network using a simple, nearest-neighbor routing algorithm, and an analytic solution to the optimal lifetime of networks meeting certain constraints. Alternative solutions considered range from those requiring global knowledge of the network to solutions using only next-neighbor knowledge. We compare the performance of all the routing algorithms in simulation.