This paper is focused on a discussion of parameters and heuristics that are expected to assist multihop routing in becoming more sensitive to node mobility. We provide a discussion concerning existing and a few proposed parameters. Moreover, the work also discusses two new heuristics based on the notion of link duration. The heuristics are compared based on a meaningful set of scenarios that attain different mobility aspects.
A first category of related work considers applying signal strength measurement at the receiver as a way to estimate the distance variation between two nodes, having as ultimate goal providing a way to build more robust paths. For instance, the @cite_8 relies on the variation of the received signal strength by a particular node to predict link breaks. @cite_4 have also applied signal strength measurement as a way to build more robust paths. applied the notion of received signal strength to track the distance variation between two nodes, in a specific time period @cite_11 . Their distance change tracking approach is a desirable feature as it can capture movement of nodes and the impact on links. However, and similarly to the other work in this category, it does not track movement patterns.
@cite_11 @cite_4 @cite_8
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This paper presents a new method for imaging, localizing, and tracking motion behind walls in real time. The method takes advantage of the motion-induced variance of received signal strength measurements made in a wireless peer-to-peer network. Using a multipath channel model, we show that the signal strength on a wireless link is largely dependent on the power contained in multipath components that travel through space containing moving objects. A statistical model relating variance to spatial locations of movement is presented and used as a framework for the estimation of a motion image. From the motion image, the Kalman filter is applied to recursively track the coordinates of a moving target. Experimental results for a 34-node through-wall imaging and tracking system over a 780 square foot area are presented. This paper presents and evaluates a method for simultaneously tracking a target while localizing the sensor nodes of a passive device-free tracking system. The system uses received signal strength (RSS) measurements taken on the links connecting many nodes in a wireless sensor network, with nodes deployed such that the links overlap across the region. A target moving through the region attenuates links intersecting or nearby its path. At the same time, RSS measurements provide information about the relative locations of sensor nodes. We utilize the Sequential Monte Carlo (particle filtering) framework for tracking, and we use an online EM algorithm to simultaneously estimate static parameters (including the sensor locations, as well as model parameters including noise variance and attenuation strength of the target). Simultaneous tracking, online calibration and parameter estimation enable rapid deployment of a RSS-based device free localization system, e.g., in emergency response scenarios. Simulation results and experiments with a wireless sensor network testbed illustrate that the proposed tracking method performs well in a variety of settings. With the technical advances in ubiquitous comput- ing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoreti- cal base and develop a localization algorithm for building a zero- configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters). Clustering is an essential tool to facilitate the function of self-organizing peer-to-peer and sensor networks. The LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm for forming clusters in ad-hoc networks represents a probabilistic way of choosing cluster-heads which shows a large variation over the average link distances of clusters and leads to uneven energy consumption during the transmission phase. However LEACH has the advantage that the energy load of being cluster-head is distributed among all the nodes resulting in a longer network life-time. On the other hand, the RSSI (Received Signal Strength Indicator)-based algorithms try to reduce the variation over the average link distances of clusters by means of choosing cluster-heads in the areas of higher node density in order to have an even energy consumption during transmission phase. But it distributes the energy load of being the cluster-head between some specific nodes which leads to a shorter network life-time. Here, we propose a new clustering scheme E-RSSI (Enhanced RSSI), which has the main advantages of the RSSI based schemes as well as LEACH. The average link distance of E-RSSI is almost the same as the average link distance of RSSI. And E-RSSI, like RSSI, has a significantly smaller variation over the average link distances in comparison with LEACH and at the same time shows significant improvement in network life-time. E-RSSI produces a low energy cost per node like RSSI and a more evenly distributed energy cost per node like LEACH therefore a prolonged network life-time.