Push & Pull: autonomous deployment of mobile sensors for a complete coverage

Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push&Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push&Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios.Comment: Technical Report. This paper has been published on Wireless Networks, Springer. Animations and the complete code of the proposed algorithm are available for download at the address: http://www.dsi.uniroma1.it/~novella/mobile_sensors

Demo: Snap – Rapid Sensornet Deployment with a Sensornet Appstore

Despite ease of deployment being seen as a primary advantage of sensor networks, deployment remains difficult. We present Snap, a system for rapid sensornet deployment that allows sensor networks to be deployed, positioned, and reprogrammed through a sensornet appstore. Snap uses a smartphone interface that uses QR codes for node identification, a map interface for node positioning, and dynamic loading of applications on the nodes. Snap nodes run the Contiki operating system and its low-power IPv6 network stack that provides direct access from nodes to the smartphone application. We demonstrate rapid sensor node deployment, identification, positioning, and node reprogramming within seconds, over a multi-hop sensornet routing path with a WiFi-connected smartphone

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Energy-efficient query management scheme for a wireless sensor database system

Minimizing the communication overhead to reduce the energy consumption is an essential consideration in sensor network applications, and existing research has mostly concentrated on data aggregation and in-network processing. However, effective query management to optimize the query aggregation plan at the gateway side is also a significant approach to energy saving in practice. In this paper, we present a multiquery management framework to support historical and continuous queries, where the key idea is to reduce common tasks in a collection of queries through merging and aggregation, according to query region, attribute, time duration, and frequency, by executing the common subqueries only once. In this framework, we propose a query management scheme to support query partitioning, region aggregation and approximate processing, time partitioning and aggregation rules, multirate queries, and historical database. In order to validate the performance of our algorithm, a heuristic routing protocol is also described. The performance simulation results show that the overall energy consumption for forwarding and answering a collection of queries can be significantly reduced by applying our query management scheme. The advantages and disadvantages of the proposed scheme are discussed, together with open research issues