A. Ahmed

A survey on network protocols for wireless sensor networks

Recent advances in MEMS (micro-electromechanical systems), processor, radio, and memory technologies have dramatically enabled development of wireless sensor networks. A sensor network is a large network of small sensor nodes, capable of sensing, communication, and computation. It can be deployed to sense some physical phenomenon for a wide variety of applications. During recent years, research in wireless sensor networks has become more and more active. Network protocols developed for sensor networks are of great importance to meet specific design goals of sensor networks. We present a survey of recent work addressing network protocols, including routing and information dissemination algorithms, for wireless sensor networks. We evaluate them in terms of design goals, assumptions, operation models, energy models, and performance metrics.

SHARP: a new approach to relative localization in wireless sensor networks

For wireless sensor networks, localization is crucial due to the dynamic nature of deployment. In relative localization, nodes use the distance measurements to estimate their positions relative to some coordinate system. In absolute localization, a few nodes (called anchors) need to know their absolute positions, and all the other nodes are absolutely localized in the coordinate system of the anchors. Relative and absolute localization methods differ in both the performance and the cost. We present a new approach to relative localization that we refer to as: simple hybrid absolute-relative positioning (SHARP). In SHARP, a relative localization method (M1) is used to relatively localize N/sub r/ reference nodes. Then, an absolute localization method (M2) uses these N/sub r/ nodes as anchors to localize the rest of the nodes. Choosing N/sub r/, M1, and M2 gives a wide range of performance-cost tuning. We have done extensive simulation using the multidimensional scaling (MDS) method as M1 and the ad-hoc positioning system (APS) method as M2. While previous research shows that MDS gives better localization results than APS, our simulation shows that SHARP outperforms MDS if both the localization error and the cost are considered.

Variants of multidimensional scaling for node localization

Recently multidimensional scaling (MDS) has been successfully applied to the problem of node localization in ad-hoc networks, such as wireless sensor networks. The MDS-MAP method uses MDS to compute a local, relative map at each node from the distance or proximity information of its neighboring nodes. Based on the local maps and the locations of a set of anchor nodes with known locations, the absolute positions of unknown nodes in the network can be computed. We investigate several variants of MDS and their effects on the accuracy of localization in wireless sensor networks. We compare metric scaling and non-metric scaling methods, each with several different optimization criteria. Simulation results show that different optimization models of metric scaling achieve comparable localization accuracy and non-metric scaling achieves more accurate results than metric scaling for sparse networks at the expense of higher computational cost.

Network-aware positioning in sensor networks

Out of its importance to various applications and services, the geographical location of the sensed event is to be associated with the event itself being reported. Despite the numerous number of localization algorithms proposed, very few of them are really ad-hoc methods that are appropriate for sensor networks. In this paper, our contribution is double-folded. First, we design an experimental framework to evaluate localization methods for sensor networks. We use this framework to evaluate three localization methods: ad-hoc positioning system (APS), multi-dimensional scaling (MDS), and semi-definite programming (SDP). Using this evaluation, we identify five network properties that affect the localization accuracy. Second, we propose an adaptive localization method that we refer to as: network-aware positioning (NAP). NAP starts by assuming known network properties. Given these properties, NAP determines the best localization algorithm to use. Simulation results show that NAP performs the best among the three algorithms under all network conditions

Map-based Adaptive Positioning in Wireless Sensor Networks

Frequent localization in sensor networks may be needed due to the dynamically changing topology and the possible mobility of sensor nodes. We present a distributed adaptive localization method that we refer to as: map-based adaptive positioning (MAP). The main idea is to construct a relative local map at every node in the network, consisting of the node itself and its immediate neighbors, and merge the local maps together to form a global map. We consider two algorithms that can be used to estimate the relative local maps: multidimensional scaling (MDS) and semidefinite programming (SDP). The performance of these algorithms depend on two parameters: size of a local map, i.e., number of nodes, and the average connectivity of the node at the center of the local map and its 1-hop neighbors. We use machine learning to adaptively select the appropriate algorithm to estimate the relative local maps. Simulation results show that MAP outperforms both MDS and SDP, with better improvement for networks with less uniform node deployment.

MDS-BASED METHODS FOR AD HOC NETWORK LOCALIZATION

Recently, multidimensional scaling (MDS) techniques have been successfully applied in the MDS-MAP method to the node localization problem of ad hoc networks, such as wireless sensor networks. MDS-MAP uses MDS to compute a local, relative map at each node from the distance or proximity information of its neighboring nodes. Based on the local maps and the locations of a set of anchor nodes with known locations, the absolute positions of unknown nodes in the network can be computed. In this paper, we investigate the effects of several variants of MDS on the accuracy of localization in wireless sensor networks. We compare metric scaling and non-metric scaling methods, each with several different optimization criteria. Simulation results show that different optimization models of metric scaling achieve comparable localization accuracy for dense networks and non-metric scaling achieves more accurate results than metric scaling for sparse networks at the expense of higher computational cost.