Santosh Pandey

ARIADNE: a dynamic indoor signal map construction and localization system

Location determination of mobile users within a building has attracted much attention lately due to its many applications in mobile networking including network intrusion detection problems. However, it is challenging due to the complexities of the indoor radio propagation characteristics exacerbated by the mobility of the user. A common practice is to mechanically generate a table showing the radio signal strength at different known locations in the building. A mobile users location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and determining the location by referring to the above table using a LMSE (least mean square error) criterion. Obviously, this is a very tedious and time consuming task. This paper proposes a novel and automated location determination method called ARIADNE. Using a two dimensional construction floor plan and only a single actual signal strength measurement, ARIADNE generates an estimated signal strength map comparable to those generated manually by actual measurements. Given the signal measurements for a mobile, a proposed clustering algorithm searches that signal strength map to determine the current mobiles location. The results from ARIADNE are comparable and may even be superior to those from existing localization schemes.

IEEE 802.11af: a standard for TV white space spectrum sharing

Spectrum today is allocated in frequency blocks that serve either licensed or unlicensed services. This static spectrum allocation has limited resources to support the exponential increase in wireless devices. In this article, we present the IEEE 802.11af standard, which defines international specifications for spectrum sharing among unlicensed white space devices (WSDs) and licensed services in the TV white space band. Spectrum sharing is conducted through the regulation of unlicensed WSDs by a geolocation database (GDB), the implementation of which differs among regulatory domains. The main difference between regulatory domains is the timescale in which WSDs are controlled by the GDB, resulting in different TVWS availability and WSD operating parameters. The IEEE 802.11af standard provides a common operating architecture and mechanisms for WSDs to satisfy multiple regulatory domains. This standard opens a new approach to treat spectrum as a single entity shared seamlessly by heterogeneous services.

CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach

With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant interest due to its high accuracy. In this paper, we present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error, compared with three existing methods in two representative indoor environments.

A survey on localization techniques for wireless networks

Abstract Wireless networks have displaced the well established and widely deployed wired communication networks of the past. Tetherless access and new services offered to mobile users contribute to the popularity of these networks. Thus users have access from many locations and can roam ubiquitously. The knowledge of the physical location of mobile user devices, such as phones, laptops and PDAs, is important in several applications such as network planning, location based services, law enforcement and for improving network performance. A devices location is usually estimated by monitoring a distance dependent parameter such as wireless signal strength from a base station whose location is known. In practical deployments, signal strength varies with time and its relationship to distance is not well defined. This makes location estimation difficult. Many location estimation or localization schemes have been proposed for networks adopting a variety of wireless technologies. This paper reviews a broad class of localization schemes that are differentiated by the fundamental techniques adopted for distance estimation, indoor vs. outdoor environments, relative cost and accuracy of the resulting estimates and ease of deployment. The paper exposes many challenges that remain and elaborates on several future research problems that need to be solved.

Secure localization in sensor networks using transmission range variation

In a wireless sensor network, sensors can be randomly distributed in order to collect data from a site. In many cases the location of the wireless sensor nodes is required in order to map the collected data to a particular location. Various schemes have been proposed earlier to estimate the location of sensor nodes in a sensor network. However most of these schemes assume the absence of a malicious user in the system which might not always be true. This paper presents a secure localization algorithm (SLA) that can be implemented with the capabilities of current sensor nodes without the need for any additional specialized hardware. The scheme is based on the transmission of nonces at different power levels from the anchor nodes. A sensor node receives a certain set of nonces which it will have to transmit back to the sink via the anchor nodes. The location of the sensor node can be estimated securely based on this set of nonces. We have investigated the properties of SLA using simulations

DeepFi: Deep learning for indoor fingerprinting using channel state information

With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.

Client assisted location data acquisition scheme for secure enterprise wireless networks

Current wireless networks that are widely deployed for various commercial applications, do not keep track of a mobile users location. However, location information can enhance security by facilitating tracking of misbehaving users, or though implementation of location based network access. Previous work done on location estimation typically involves methods that use a lookup table comprising the client or access point signal strengths, which are collected manually at short distances throughout the site. This paper proposes a new data collection scheme for building a lookup table using client assistance in an enterprise wireless environment. In this work, a network-bused location estimation scheme is implemented using sniffers, which monitor client signal strength. It is observed that the accuracy of location estimation is improved by averaging the lookup table data collected over time. The client assisted data collection scheme can be used to frequently build lookup tables in an efficient manner and hence improve accuracy in location estimation. Thus, this scheme could be used to implement a location based security policy throughout the enterprise wireless network.

Localization of sensor networks considering energy accuracy tradeoffs

Localization in sensor networks is a prerequisite for associating the sensed phenomenon to a particular location. Various aspects of sensor network localization have recently been studied. However, amongst the vast number of localization schemes proposed, tradeoff between the energy required for localization and resulting accuracy of localization is not addressed. In a highly resource-constrained environment such as a sensor network, it is important to address the above energy accuracy relationship during the localization process. Network lifetime is enhanced by applying energy-aware localization schemes periodically. In this paper, a localization algorithm is presented which transforms the network into a system of springs. Using optimization techniques the stable state of such a system is determined, which yields the locations of the nodes in the network. The energy accuracy tradeoff obtained from the algorithm is studied using MATLAB simulations. Results for a centralized and distributed implementation of the scheme are presented.

A Hybrid Approach to Optimize Node Placements in Hierarchical Heterogeneous Networks

In this paper, we address the problem of node placement in a hierarchical heterogeneous wireless sensor network. We consider a two-tiered wireless sensor network where the resource constrained lite nodes (LNs) are used for sensing the environment and high-end sophisticated nodes (SNs) are added to aggregate and forward data. We intend to place minimum number of SNs to handle the traffic generated by LNs and ensure that the SNs form a connected network. We formulate the node placement problem as an optimization problem and use three different algorithms to solve it; namely, binary integer linear programming (BILP), greedy algorithm (GREEDY) and genetic algorithm (GA). We also propose a hybrid approach (HYBRID) combining BILP, GREEDY and GA to improve results. It was found through simulations that GA performed better for random LN deployment. However, using HYBRID, results comparable to original GA could be obtained in only 11.46 % of the time required for the original GA. We support the results with statistical tests.

A low-cost robust localization scheme for WLAN

Localization in WLAN networks has been an active area of research recently. However, most of the previous schemes in this area do not consider the presence of any malicious user within the network. The growing interest in location based services would necessitate the development of a low-cost localization scheme which is robust against the attacks from these malicious users. In this paper, we propose such a low-cost secure localization scheme. The proposed scheme is based on the current access point (AP) capability of transmitting at different power levels. The main idea here is to leverage this capability of APs so that a unique set of the messages transmitted by various APs can be received at every location in the system. The client is expected to transmit back the received messages to the AP it is associated with. The location of the client is then estimated using the set of messages received from the client. In this paper we focus more on the performance of this scheme while discussing briefly its inherent security capabilities. We also describe the implementation of this message based scheme. We then perform extensive experiments and observe that this scheme has similar or better localization accuracy as compared to the traditional signal strength based localization schemes.