Tree-based object tracking without mobility statistics in wireless sensor networks

Abstract

Object tracking in wireless sensor networks is to track mobile objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object's move. Existing tree construction algorithms all require a mobility profile that characterizes the movement statistics of the target object. Mobility profiles are generally obtained based on historical running traces. The contribution of this work is twofold. We first show that the problem of finding an optimal message report tree that requires the least amount of report messages is NP-hard. We then propose analytic estimates of mobility profiles based on Markov-chain model. This profiling replaces an otherwise experimental process that generates and analyzes running traces. Simulation results show that the analytic profiling works well and can replace costly statistical profiling without noticeable performance degradation.

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

“…In Fig. 10, shows the number of published articles that are energy efficient, cluster-based network has the highest number of papers that are energy efficient [117], [121], [122], [56], [87], [106] followed by the tree-based networks [123], [124]. The methods are energy efficient only in situations where the target speed is constant.…”

Optimal nearest neighbor queries in sensor networks

“…Yet another line of work [2][3][4][5][6] considered the problem of tracking moving objects in sensor networks using (minimum cost) spanning trees. However, these approaches allow only the root node of the spanning tree, i.e.…”

“…Due to the use of spanning tree variations, their performance bounds can be as large as O(D); which is significantly larger compared to our solution. Also, it has been shown in [5,6] that constructing an optimal spanning tree that minimizes the update and/or query cost is NP-hard. Moreover, recent work [13][14][15]26,27] also studied moving object tracking for several other applications, including distributed transactional memory and distributed queuing, but these approaches are analyzed assuming only one object in the whole network and it is yet to figure out how they can be extended to support multiple objects efficiently.…”

“…can be detected by nearby sensors [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. This application is interesting and arises in a setting where a system of static sensor nodes and a set of mobile objects are working in the same physical plane [12].…”

“…This is different from our objective of finding the nearest mobile object from any sensor node at any time. Authors in [2][3][4][5][6] focused on tracking mobile objects but queries are only supported from a specific sink node. These papers [10,13,26,9,27] tried to solve the NNQ problem but with only one object; for multiple objects the structure developed should be replicated for every object.…”