Cooperative Abnormality Detection via Diffusive Molecular Communications

Abstract

In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects into the medium a number of molecules which is proportional to the sensed value. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, referred to as DTM, each sensor releases a different type of molecule, whereas in the second scheme, referred to as STM, all sensors release the same type of molecule. The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the trade-off between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

Early Cancer Detection in Blood Vessels Using Mobile Nanosensors

“…In [18], anomaly detection in molecular nanonetworks is studied and a suboptimal decision rule is employed to combine the observations at the FC. More recently, in [19], both optimal and near-optimal decision rules are developed for networks where nanosensors employ either one or multiple types of molecules to relay their gathered information to the FC. However, both [18] and [19] assume that the nanosensors are fixed, i.e., they do not move inside the CS.…”

“…More recently, in [19], both optimal and near-optimal decision rules are developed for networks where nanosensors employ either one or multiple types of molecules to relay their gathered information to the FC. However, both [18] and [19] assume that the nanosensors are fixed, i.e., they do not move inside the CS. In [20], an MC system for tumor detection in blood vessels is proposed.…”

Sequential Decision Fusion for Abnormality Detection via Diffusive Molecular Communications

“…Employing dedicated molecules as information carriers, diffusion based MC encodes information into some aspect of the released molecules, such as the release time, the number or the type of those molecules [1].This paper focuses on the task of abnormality detection i.e. the detection and reporting of abnormal events that may characterize the presence of a disorder in a fluid environment, employing an MC based nanoscale sensor network [2]. Such distributed detection (DD) problems lie in the heart of the most highly anticipated applications of nanoscale networks, such as health monitoring, disease diagnosis, targeted drug delivery, environmental sensing and monitoring, contaminant and toxic agent detection, environmental remediation and many more.…”

Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication

“…Recently, [17][18][19] considered cooperative ML detection for MC. However, in [17,18] the RXs communicate with an FC but do not detect information from a TX. Also, in [18,19] the FC makes a single decision about the presence of an abnormality, such that there is only one information symbol and no symbol-by-symbol detection.…”