Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail

We are motivated by the problem of impromptu or as- you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201

A Routing Algorithm for Extending Mobile Sensor Network’s Lifetime using Connectivity and Target Coverage

In this paper, we propose an approach to improving the network lifetime by enhancing Network CONnectivity (NCON) and Target COVerage (TCOV) in randomly deployed Mobile Sensor Network (MSN). Generally, MSN refers to the collection of independent and scattered sensors with the capability of being mobile, if need be. Target coverage, network connectivity, and network lifetime are the three most critical issues of MSN. Any MSN formed with a set of randomly distributed sensors should be able to select and successfully activate some subsets of nodes so that they completely monitor or cover the entire Area of Interest (AOI). Network connectivity, on the other hand ensures that the nodes are connected for the full lifetime of the network so that collection and reporting of data to the sink node are kept uninterrupted through the sensor nodes. Keeping these three critical aspects into consideration, here we propose Socratic Random Algorithm (SRA) that ensures efficient target coverage and network connectivity alongside extending the lifetime of the network. The proposed method has been experimentally compared with other existing alternative mechanisms taking appropriate performance metrics into consideration. Our simulation results and analysis show that SRA performs significantly better than the existing schemes in the recent literature

Embracing Localization Inaccuracy: A Case Study

In recent years, indoor localization has become a hot research topic with some sophisticated solutions reaching accuracy on the order of ten centimeters. While certain classes of applications can justify the corresponding costs that come with these solutions, a wealth of applications have requirements that can be met at much lower cost by accepting lower accuracy. This paper explores one specific application for monitoring patients in a nursing home, showing that sufficient accuracy can be achieved with a carefully designed deployment of low-cost wireless sensor network nodes in combination with a simple RSSI-based localization technique. Notably our solution uses a single radio sample per period, a number that is much lower than similar approaches. This greatly eases the power burden of the nodes, resulting in a significant lifetime increase. This paper evaluates a concrete deployment from summer 2012 composed of fixed anchor motes throughout one floor of a nursing home and mobile units carried by patients. We show how two localization algorithms perform and demonstrate a clear improvement by following a set of simple guidelines to tune the anchor node placement. We show both quantitatively and qualitatively that the results meet the functional and non-functional system requirements