IEEE 2017-2018 Project Titles on RSSI

Abstract:

Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods, such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources, such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using adaptive fingerprint (MUFAF) algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a target position estimation (TPE) process is performed by every sensor; second, a target tracking process stage; third, a multi-sensor fusion combines the sensor information; and finally, an adaptive fingerprint update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel density estimation technique is employed to obtain object position. A Modified Kalman Filter is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this paper: track-to-track fusion and Kalman sensor group fusion. Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a test bed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.

Abstract:

Wireless sensor networks have extensively been utilized over the years for ambient data collection from diverse structural deployments including mesh, ad hoc, and hierarchical layouts. Several other applications of sensor networks may involve placing the nodes in a linear topology, constituting a special class of networks called linear sensor networks. In a densely deployed linear network case, issues related to optimal resource allocation and networking may persist because the standard sensor network protocols attempt to manage the network as a mesh or ad hoc infrastructure. Issues like recovering from holes where a node cannot reach another node in either side or policies to establish a route for data dissemination need to be intelligently solved for linear sensor networks. To solve such issues, we propose a linear sensor network deployment application for oil and gas pipeline using a custom sensor board accompanied with algorithms to solve network creation, leak interrupt detection, and routing of high-priority messages with reliability while keeping network alive at all times. The proposed system provides all the features of leakage detection, localization, parameter sensing, and actuation, while operating at low energy, high data reliability, and low latencies, while comparative results prove the efficacy of the system.

Abstract:

Received signal strength indicator (RSSI) gives a rough initial measure of the inter-node distances at low cost without the need of additional equipment or complexity. This necessitates the need for a mechanism to obtain accurate node locations from the noisy RSSI distance estimates. In this paper, a non-linear manifold learning technique, adaptive locally linear embedding (ALLE), is proposed for node localization using the noisy RSSI distance estimates. ALLE, a modified version of LLE, considers the neighborhood around a node to determine the neighbors to approximate the node optimally. Experimental and simulation results show that ALLE is able to localize the nodes accurately in both clustered and centralized wireless sensor network. The centralized mechanism is found to have higher accuracy as compared with ALLE running on different cluster heads. However, this increase in accuracy is at the cost of significant energy overhead required for information gathering at the base station. Results also indicate that the ALLE is able to localize sensor nodes with an increased accuracy of around 9.38% as compared with native LLE.

Abstract:

In recent years, the mobility market has experienced a phenomenal growth in the productivity of electronic equipment and development of new telecommunication services. This has given birth to the idea of providing services based on the user's position in several sectors. Although Global Positioning System (GPS) considered as the best solution for an open-air localization (Outdoor), it is inaccurate in urbanized and indoor environments and adaptation of such systems to those contexts are particularly challenging due to the disability of GPS signal to penetrate buildings because it needs to be in sight. Various indoor localization techniques were conducted to provide the best solution to deploy. This paper describes an implementation of Wi-Fi fingerprinting method using RSSI (Received Signal Strength Indicator) from access points to determine the position of users in indoor areas.

Abstract:

Passive acoustic methods can detect, track and classify surface and underwater vessels. We consider the application of these systems for DHS interests with attention to small boats. We present a review of various methods of detection and tracking for low-cost low-power systems with a few hydrophones. The analysis of acoustic signatures of boats to determine their physical parameters (shaft and engine rate, number of propellers and blades, and engine firing rate) is discussed in detail. These parameters can be used for vessel classification. The analysis used acoustic recordings of boats from the Stevens acoustic library.

Abstract:

Recently, as elderly people population grows, the burdens on caretakers are getting larger. In daycare centers, caretakers make a daycare report aiming to improve the senior citizen's Quality of Life. However, in the present situation, it is difficult for caretakers to record the senior citizen's activity in detail, since each caretaker needs to take care of several senior citizens at the same time. To reduce the burden of caretakers, many elderly monitoring systems have been proposed so far, but most of them are not effective in the sense that they force the senior citizen to use dedicated devices such as smart phone and/or particular applications that are obtrusive and cumbersome for care receivers. In this paper, we propose a semi-automatic care-taking report generation system which can monitor movements/activity of senior citizens in daycare centers. Our proposed system estimates multiple locations (areas) where senior citizens are located with the BLE beacon, by utilizing RSSI of the Bluetooth radio wave. Also, the accelerometer implemented in the tag estimates the activity of the elderly. The information of the estimated area and activity is stored in a server with time stamp. The server generates the daycare report based on it. In order to evaluate the proposed system, we have deployed our system in a daycare center: Ikoi-no-ie 26. Evaluation result in Ikoi-no-ie 26 showed that our system estimated the subject's present area with F-measure: 80.6% and activity with F-measure: 73.8% and generated the daycare report.

Abstract:

Securing a wireless channel between any two vehicles is a crucial component of vehicular networks security. This can be done by using a secret key to encrypt the messages. We propose a scheme to allow two cars to extract a shared secret from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same key. The key is information-theoretically secure, i.e., it is secure against an adversary with unlimited computing power. Although there are existing solutions of key extraction in the indoor or low-speed environments, the unique channel conditions make them inapplicable to vehicular environments. Our scheme effectively and efficiently handles the high noise and mismatch features of the measured samples so that it can be executed in the noisy vehicular environment. We also propose an online parameter learning mechanism to adapt to different channel conditions. Extensive real-world experiments are conducted to validate our solution.