Chan-Myung Kim

Regular sensor deployment patterns for p-coverage and q-connectivity in wireless sensor networks

In this paper, we study the regular sensor deployment patterns to achieve p-coverage and q-connectivity (q ≤ 6) under different ratios of sensor nodes communication range (rc) to their sensing range (rs) for wireless sensor networks. In particular, we propose the triangular lattice, square grid, and hexagon deployment patterns to achieve p-coverage and q-connectivity. To form such regular deployment patterns for given p and q values, we present the distance between sensor nodes as well as the relationship between rc and rs. We will get this information by using the inverse method of the proposed theorems which estimate the coverage level of the target region when sensors are deployed. We also compare the efficiency of the proposed regular deployment patterns in terms of the number of sensors needed to provide p-coverage and q-connectivity.

Betweenness of Expanded Ego Networks in Sociality-Aware Delay Tolerant Networks

Recently, the consideration of social characteristics present a new angle of view in the design of data routing and dissemination in delay tolerant networks. Many social network studies have been undertaken using a ego network because it has the benefit of simplicity in data collection and gives statistically significant features about the entire network in many cases. In this paper, we newly define the expanded ego network by comprising the ego’s 2-hop neighbor nodes as well as the ego’s 1-hop ones. In delay tolerant networks, the expended ego network can be easily self-configured at a node and it can contain more network information than the ego network. Therefore, it is expected that the effectiveness of the expanded ego network will be higher than the one of the ego network in terms of data routing and dissemination. We examine that the relationship among the expanded ego betweenness, the ego betweenness, and the betweenness of the entire network for a node. By a simulation study, we show that the expanded ego betweenness is highly correlated with the betweenness of the entire network when the network is dense and its nodes are highly inter-related.

An Energy-Efficient Self-Deployment Scheme in Intelligent Mobile Sensor Networks

In traditional static wireless sensor networks, it is sometimes impossible to deploy the sensors manually when they are distributed in unexploited, hostile, or disaster areas. If each sensor has the locomotion capability, it can re-deploy itself using the location information of neighbor sensors. In our previous study, we showed that moving sensors to the centroids of their Voronoi polygon generated by the location information of neighbor sensors is efficient for expanding the coverage area. In this paper, we present an improved potential-field-based selfdeployment scheme to utilize the attractive force generated from the centroid of a sensors local Voronoi polygon as well as the repulsive force usually used in the traditional potential-fieldbased scheme. Simulation results show that our scheme can achieve higher coverage and less movement in shorter time than the traditional potential-field-based scheme.

An Efficient Routing Scheme Based on Social Relations in Delay-Tolerant Networks

In delay-tolerant network (DTN), the message forwarding and routing are important research issues, since the network topology changes dynamically and there is no guarantee of continuous connectivity between any two nodes. In this paper, we propose an efficient DTN routing scheme by using a node’s social relation where each node chooses a proper relay node based on its contact history. In order to enhance the routing efficiency, the expanded ego-network betweenness centrality is used when a relay node is selected. We have demonstrated that our algorithm performs efficiently compared to the existing epidemic and friendship routing schemes.

A Socially Aware Routing Based on Local Contact Information in Delay-Tolerant Networks

In delay-tolerant networks, network topology changes dynamically and there is no guarantee of continuous connectivity between any two nodes. These features make DTN routing one of important research issues, and the application of social network metrics has led to the design of recent DTN routing schemes. In this paper, we propose an efficient routing scheme by using a nodes local contact history and social network metrics. Each node first chooses a proper relay node based on the closeness to the destination node. A locally computed betweenness centrality is additionally utilized to enhance the routing efficiency. Through intensive simulation, we finally demonstrate that our algorithm performs efficiently compared to the existing epidemic or friendship routing scheme.

A scheduling algorithm for connected target coverage under probabilistic coverage model

Connected Target Coverage (CTC) problem [8], covering given targets fully with the deployed sensors and also guaranteeing connectivity to a sink node, is a challenging scheduling problem. In this paper, unlike the existing heuristic algorithms, we adopt the probabilistic coverage model to solve the problem and develop a heuristic algorithm called CWGC-PM (Communication Weighted Greedy Cover-Probabilistic Model) to extend the network lifetime while such coverage and connectivity constraints are satisfied. Simulation results are presented to evaluate the performance of the proposed algorithm and they show that the probabilistic coverage model can capture the diverse sensing characteristics of sensor nodes in the real world.

A Greedy Algorithm to Extend the Lifetime of Randomly Deployed Directional Sensor Networks

Directional sensor networks (DSNs) comprise a large number of sensors equipped with a limited battery and limited angles of sensing range. In DSNs, maximizing network lifetime while covering all the targets in a given area is still a challenge problem. A major technique to save the energy power of sensors is to use a node wake-up scheduling protocol by which some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy. In this paper, we first address the MSCD (Maximum Set Cover for DSNs) problem that is known as NP-complete and then present a new target coverage scheduling scheme to solve this problem with a greedy algorithm. To verify and evaluate the proposed scheme, we conduct simulations and show that it can contribute to extending the network lifetime largely. By the simulations, we also present an energy-efficient strategy to choose a sensor in order to organize a scheduling set in the greedy scheme.

An efficient strategy of nonuniform sensor deployment in cyber physical systems

In this paper, we study the sensor deployment pattern problem in cyber physical systems. When designing the sensor deployment pattern, the network lifetime maximization while covering the given area/targets and forwarding sensor data to a sink node is an important issue. In order to prolong the network lifetime by balancing energy depletion across all sensors, we propose a novel nonuniform sensor distribution strategy. Since sensors located closer to the sink are more involved in data forwarding, sensor densities in different areas should be varied according to the distance to the sink. Based on the nonuniform sensor distribution, we propose sensor deployment patterns to satisfy the coverage and connectivity requirements and prolong the network lifetime. A numerical computation is performed to validate and compare the effectiveness of the proposed deployment patterns.

A Community Detection Scheme in Delay-Tolerant Networks

Social analysis and community structure detection present a new angle of view in the design of data routing and dissemination in delay tolerant networks (DTNs). However, most community detection schemes developed in the context of sociology cannot be applied to DTNs since they have been designed without the consideration of the dynamic aspect of DTNs where the network topology changes and evolves over times. In this paper, we propose a new community detection algorithm which runs in a distributed and real-time manner and thus can be used in time-varying networks like DTNs. From the performance study, we show that our algorithm well adapts to dynamically changing networks.

Efficient Estimation of Betweenness Centrality in Wireless Networks

In wireless networks, the betweenness of a node has been considered an indication of that node’s importance in efficiently and reliably delivering messages. In a large wireless network, however, the cost of computing the betweenness of every node is impractically high. In this paper, we introduce a new representation of a node’s vicinity, called the expanded ego network (shortly, x-ego network) of that node. We also propose an approach that calculates the x-ego betweenness of a node (i.e., the betweenness of that node in its x-ego network) and use it as an estimate of the true betweenness in the entire network. Furthermore, we develop an algorithm that quickly computes x-ego betweenness by exploiting structural properties of x-ego networks. Our evaluation results show the benefits and effectiveness of the above approach using trace data obtained from real-world wireless networks.