Jing (Selena) He's research focuses on the design and analysis of algorithms and protocols for wireless ad hoc networks, wireless sensor networks, and probabilistic wireless networks. The research topics include Connected-Dominating-Set-based backbone construction, target coverage, query scheduling, network capacity analysis, and etc.
There are no fixed or pre-defined infrastructures in wireless networks and usually there is no central management either. Therefore, virtual backbones are in necessity for wireless networks. Connected Dominating Set (CDS) is a promising candidate to serve as a virtual backbone stimulated by the characteristics of wireless networks. It can help with topology control, routing, and many applications in wireless networks. We study how to construct and maintain CDSs considering several constraints such as size, diameter, load-balance, fault tolerance, routing flexibility and etc.
Usually, Wireless Sensor Networks (WSNs) are modeled using the Deterministic Network Model (DNM). Under this ideal model, any pair of nodes in a network is either fully connected or completely disconnected. In most real applications, however, the DNM model cannot fully characterize the behavior of wireless links. This is mainly due to the transitional region phenomenon which has been revealed by many empirical studies. Beyond the “always connected” region, there is a transitional region where a pair of nodes are probabilistically connected. Such pairs of nodes are not fully connected but reachable via the so called lossy links. In some situations, there are often much more lossy links than fully connected links. Therefore, their impact can hardly be neglected.
Energy efficiency is one of the primary concerns for wireless sensor networks. Prolonging network lifetime in terms of saving energy is an important system design goal in wireless sensor networks. One of the most prominent method is based on scheduling sensor activities so that a subset of active sensors, instead of all the sensors, can carry out a task while the rest of the redundant sensors can go to the sleep mode for energy conservation. The coverage problems intend to seek such a method in order to maximize network lifetime while satisfying the coverage requirements. We investigate the problems of area coverage, target coverage, k-coverage, partial coverage, and coverage scheduling.
Query is a fundamental technique to get user interested information from wireless sensor networks. Query scheduling is an essential yet time-consuming operation to collect query results.Communication collision is a primary reason for long latency in query data collection. To find a minimum-latency schedule is an NP-hard problem. We spend effort in designing minimum-latency query scheduling algorithms to decide a collision free transmission schedule of query data collection for all the sensors such that the total time latency for collected data to reach the sink is minimized.
WSNs are mainly used for gathering data from the physical world to a sink (base station). During a data gathering process, if the raw data of all the sensors are transmitted to the sink, it is called the data collection. If the raw data can be aggregated and only an aggregation value is transmitted to the sink, it is called the data aggregation. Furthermore, the union of all the data from all the sensors at a particular time instant is called a snapshot. The problem of collecting the aggregated value of one snapshot is called Snapshot Data Aggregation(SDA). The problem of collecting the aggregated value of each snapshot of multiple continuous snapshots is called Continuous Data Aggregation (CDA). Network capacity, which can reflect the achievable data gathering, is used to evaluate network performance. For SDA and CDA, we use the ratio between the amount of data been aggregated and the time used to transmit the aggregated values of these data to the sink, referred to as SDA capacity and CDA capacity respectively, to measure their achievable network capacity. We analyze the achievable network capacity for SDA and CDA in both deterministic WSNs and probabilistic WSNs.