15 research outputs found
A Forward-connection Topology Evolution Model in Wireless Sensor Networks
The stability and reliability of the topology structure play an important role in the efficiency of the data collecting for wireless sensor networks. In this paper, a topology evolution model is proposed. The model considers the directionality of the data flow, and adopts the forward connectionism to ensure the neighbor nodes of each node. Furthermore, the model considers the balanced energy overhead in each communication path, adopts the energy balanced mechanism to compute the connection probability to the neighbor nodes. Meanwhile, the process of topology evolution is distributed and the communication radiuses of all sensor nodes are limited. A theoretical analysis exhibits that the model has power-law distribution of node degrees. Simulation shows that the proposed topology evolution model make energy overhead more balanced, and prolongs the lifetime of the network
Connectivity in Secure Wireless Sensor Networks under Transmission Constraints
In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key
pre-distribution scheme is a widely recognized way to secure communications.
Although connectivity properties of secure WSNs with the EG scheme have been
extensively investigated, few results address physical transmission
constraints. These constraints reflect real-world implementations of WSNs in
which two sensors have to be within a certain distance from each other to
communicate. In this paper, we present zero-one laws for connectivity in WSNs
employing the EG scheme under transmission constraints. These laws help specify
the critical transmission ranges for connectivity. Our analytical findings are
confirmed via numerical experiments. In addition to secure WSNs, our
theoretical results are also applied to frequency hopping in wireless networks.Comment: Full version of a paper published in Annual Allerton Conference on
Communication, Control, and Computing (Allerton) 201
Localized and Configurable Topology Control in Lossy Wireless Sensor Networks
Recent empirical studies revealed that multi-hop wireless networks like wireless sensor networks and 802.11 mesh networks are inherently lossy. This finding introduces important new challenges for topology control. Existing topology control schemes often aim at maintaining network connectivity that cannot guarantee satisfactory path quality and communication performance when underlying links are lossy. In this paper, we present a localized algorithm, called Configurable Topology Control (CTC), that can configure a network topology to different provable quality levels (quantified by worst-case dilation bounds in terms of expected total number of transmisssions) required by applications. Each node running CTC computes its transmission power solely based on the link quality information collected within its local neighborhood and does not assume that the neighbor locations or communication ranges are known. Our simulations based on a realistic radio model of Mica2 motes show that CTC yields configurable communication performance and outperforms existing topology control algorithms that do not account for lossy links
k-Connectivity in Random Key Graphs with Unreliable Links
Random key graphs form a class of random intersection graphs and are
naturally induced by the random key predistribution scheme of Eschenauer and
Gligor for securing wireless sensor network (WSN) communications. Random key
graphs have received much interest recently, owing in part to their wide
applicability in various domains including recommender systems, social
networks, secure sensor networks, clustering and classification analysis, and
cryptanalysis to name a few. In this paper, we study connectivity properties of
random key graphs in the presence of unreliable links. Unreliability of the
edges are captured by independent Bernoulli random variables, rendering edges
of the graph to be on or off independently from each other. The resulting model
is an intersection of a random key graph and an Erdos-Renyi graph, and is
expected to be useful in capturing various real-world networks; e.g., with
secure WSN applications in mind, link unreliability can be attributed to harsh
environmental conditions severely impairing transmissions. We present
conditions on how to scale this model's parameters so that i) the minimum node
degree in the graph is at least k, and ii) the graph is k-connected, both with
high probability as the number of nodes becomes large. The results are given in
the form of zeroone laws with critical thresholds identified and shown to
coincide for both graph properties. These findings improve the previous results
by Rybarczyk on the k-connectivity of random key graphs (with reliable links),
as well as the zero-one laws by Yagan on the 1-connectivity of random key
graphs with unreliable links.Comment: Published in IEEE Transactions on Information Theor