The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

Abstract—In this paper, our goal is to develop approaches to reduce the energy consumption in Radio Tomographic Imaging (RTI)-based methods for device free localization without giving up localization accuracy. Our key idea is to only measure those links that are near the current location of the moving object being tracked. We propose two approaches to find the most effective links near the tracked object. In our first approach, we only consider links that are in an ellipse around the current velocity vector of the moving object. In our second approach, we only consider links that cross through a circle with radius r from the current position of the moving object. Thus, rather than creating an attenuation image of the whole area in RTI, we only create the attenuation image for effective links in a small area close to the current location of the moving object. We also develop an adaptive algorithm for determining r. We evaluate the proposed approaches in terms of energy consumption and localization error in three different test areas. Our experimental results show that using our approach, we are able to save 50 % to 80 % of energy. Interestingly, we find that our radius-based approach actually increases the accuracy of localization. I.

Topology control algorithms in mobile ad-hoc networks aim to reduce the power consumption while keeping the topology connected. These algorithms can preserve network resources and increase network capacity. However, few efforts have focused on the issue of topology control in presence of node mobility. One of the notable mobility aware topology control protocols is the ldquomobility aware distributed topology control protocolrdquo. The main drawback of this protocol is on its mobility prediction method. This prediction method assumes linear movements and is unable to cope with sudden changes in the mobile node movements. In this paper, we propose a pattern matching based mobility prediction method in which every mobile node predicts its future location through finding similar patterns in its history of movements. Simulation results show significant improvements in terms of prediction accuracy and power consumption compared to the other known algorithms.