Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies

Wireless sensor networks (WSNs) can be used by the military for a number of purposes such as monitoring or tracking the enemies and force protection. Unlike commercial WSNs, a tactical military sensor network has different priority requirements for military usage. Especially in the remote large-scale network, topology, self-configuration, network connectivity, maintenance, and energy consumption are the challenges. In this paper, we present an overview of application scenarios in remote large-scale WSNs focusing on the primary requirements for tactical environments. We propose a sensor network architecture based on the cluster-tree based multi-hop model with optimized cluster head election and the corresponding node design method to meet the tactical requirements. With the proposed WSN architecture, one can easily design the sensor network for military usage in remote large scale environments.

Wireless sensor network design for tactical military applications : Remote large-scale environments

Key management is one of the fundamental building blocks of security services. In a network with resource constrained nodes like sensor networks, traditional key management techniques, such as public key cryptography or key distribution center (e.g., Kerberos), are often not effective. To solve this problem, several key pre-distribution schemes have been proposed for sensor networks based on random graph theory. In these schemes, a set of randomly chosen keys or secret information is pre-distributed to each sensor node and a network is securely formed based on this information. Most of the schemes assumed that the underlying physical network is dense enough, that is, the degree of each node is hig.In this paper, we revisit the random graph theory and use giant component theory by Erdos and Renyi to show that even if the node degree is small, most of the nodes in the network can be connected. Further, we use this fact to analyze the Eschenhauer et. al's, Du et. al's, and Chan et. al's key pre-distribution schemes and evaluate the relation between connectivity, memory size, and security. We show that we can reduce the amount of memory required or improve security by trading-off a very small number of isolated nodes. Our simulation results show that the communication overhead does not increase significantly even after reducing the node degree. In addition, we present an approach by which nodes can dynamically adjust their transmission power to establish secure links with the targeted networked neighbors. Finally, we propose an effcient path-key identification algorithm and compare it with the existing scheme.

Revisiting random key pre-distribution schemes for wireless sensor networks

Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Clustering is a key routing technique used to reduce energy consumption. Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, saves energy and thus prolonging network lifetime. In this paper, we propose an energy efficient cluster-head selection algorithm for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes. Our proposed model is extended to the LEACH’s stochastic cluster-head selection algorithm by modifying the probability of each node to become cluster-head based on remaining energy level of sensor nodes for transmission. Simulation results show that our proposed model could better implement load balance and prolong the lifetime of the network.

An Energy Efficient Cluster-Head Selection for Wireless Sensor Networks

Most of the research in energy efficient data gathering in data centric applications of wireless sensor networks is motivated by LEACH scheme which by allowing rotation of clusterhead role among the sensor nodes tries to distribute the energy consumption over the network. Selection of sensor node for such role rotations greatly affects the energy efficiency of the network. Hence many proposals are reported in the literature suggesting different clusterhead selection strategies. However, till now, these strategies have not been talked about for their collective understanding. So, efforts have been made, in this paper, to discuss them in unison and present a global view. Comparison of their costs of clusterhead selection in different rounds and after effects like cluster formation, distribution of clusterheads and creation of clusters shows a need of more energy efficient clusterhead selection strategy.

Clusterhead selection in clustering algorithms for wireless sensor networks: A survey

A wireless network consisting of a large number of small sensors with limited battery power can be an effective tool for gathering data in a variety of environments. Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, may save energy. In this paper, we propose an optimal energyadaptive clustering algorithm which is motivated from the LEACH protocol presented in [4]. We optimize LEACHs random cluster-head selection algorithm to ensure the balanced energy depletion over the whole network thus prolongs the network lifetime. Simulation results show that our algorithm outperforms LEACH by about 20% to 35% when 1%, 50%, 100% of nodes die for different network sizes and topologies.

Haibin Yu

Papers

Energy Adaptive Cluster-Head Selection for Wireless Sensor Networks