Evaluation and Improvement of the RSSI-based Localization Algorithm: Received Signal Strength Indication (RSSI)
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Context: Wireless Sensor Networks (WSN) are applied to collect information by distributed sensor nodes (anchors) that are usually in fixed positions. Localization (estimating the location of objects) of moving sensors, devices or people which recognizes the location’s information of a moving object is one of the essential WSN services and main requirement. To find the location of a moving object, some of algorithms are based on RSSI (Received Signal Strength Indication). Since very accurate localization is not always feasible (cost, complexity and energy issues) requirement, RSSI-based method is a solution. This method has two specific features: it does not require extra hardware (cost and energy aspects) and theoretically RSSI is a function of distance.
Objectives: In this thesis firstly, we develop an RSSI-based localization algorithm (server side application) to find the position of a moving object (target node) in different situations. These situations are defined in different experiments so that we observe and compare the results (finding accurate positioning). Secondly, since RSSI characteristic is highly related to the environment that an experiment is done in (moving, obstacles, temperature, humidity …) the importance and contribution of “environmental condition” in the empirical papers is studied.
Methods: The first method which is a common LR (Literature Review) is carried out to find out general information about localization algorithms in (WSN) with focus on the RSSI-based method. This LR is based on papers and literature that are prepared by the collaborating company, the supervisor and also ad-hoc search in scientific IEEE database. By this method as well as relevant information, theoretical algorithm (mathematical function) and different effective parameters of the RSSI-based algorithm are defined. The second method is experimentation that is based on development of the mentioned algorithm (since experiment is usually performed in development, evaluation and problem solving research). Now, because we want to compare and evaluate results of the experiments with respect to environmental condition effect, the third method starts. The third method is SMS (Systematic mapping Study) that essentially focuses on the contribution of “environmental condition” effect in the empirical papers.
Results: The results of 30 experiments and their analyses show a highly correlation between the RSSI values and environmental conditions. Also, the results of the experiments indicate that a direct signal path between a target node and anchors can improve the localization’s accuracy. Finally, the experiments’ results present that the target node’s antenna type has a clear effect on the RSSI values and in consequence distance measurement error. Our findings in the mapping study reveal that although there are a lot of studies about accuracy requirement in the context of the RSSI-based localization, there is a lack of research on the other localization requirements such as performance, reliability and stability. Also, there are a few studies which considered the RSSI localization in a real world condition.
Conclusion: This thesis studies various localization methods and techniques in WSNs. Then, the thesis focuses on the RSSI-based localization by implementing one algorithm and analyzing the experiments’ results. In our experiments, we mostly focus on environmental parameters that affect localization’s accuracy. Moreover, we indicate some areas of research in this context which need more studies.
Place, publisher, year, edition, pages
2015. , 109 p.
RSSI algorithm, indoor localization, Wireless Sensor Network (WSN), RSSI filtering, RSSI distance error, localization algorithm
IdentifiersURN: urn:nbn:se:bth-10468OAI: oai:DiVA.org:bth-10468DiVA: diva2:844785
E-LYSIS s.r.l. Milan, Italy
Subject / course
PA2534 Master's Thesis (120 credits) in Software Engineering
PAAPT Master of Science Programme in Software Engineering
Unterkalmsteiner, Michael, Dr.
Börstler, Jürgen, Professor