Routing in Delay Tolerant Networks

Delay-tolerant networks (DTNs) have the great potential to connecting devices and regions of the world that are presently under-served by current networks. A vital challenge for Delay Tolerant Networks is to determine the routes through the network without ever having an end to end path, or knowing which routers will be connected at any given instant of time. The problem has an added constraint of limited size of buffers at each node. This situation limits the applicability of traditional routing techniques which categorize lack of path as failure of nodes and try to seek for existing end-to-end path. Approaches have been proposed which focus either on epidemic message replication or on previously known information about the connectivity schedule. The epidemic approach, which is basically a flooding technique, of replicating messages to all nodes has a very high overhead and does not perform well with increasing load. It can, however, operate without any prior information on the network configuration. On the other hand, the alternatives, i.e., having a prior knowledge about the connectivity, seems to be infeasible for a self-configuring network. In this project we try to maximize the message delivery rate without compromising on the amount of message discarded. The amount of message discarded has a direct relation to the bandwidth used and the battery consumed. The more the message discarded more is the bandwidth used and battery consumed by every node in transmitting the message. At the same time, with the increase in the number of messages discarded, the cost for processing every message increases and this adversely affects the nodes. Therefore, we have proposed an algorithm where the messages are disseminated faster into the network with lesser number of replication of individual messages. The history of encounter of a node with other nodes gives noisy but valuable information about the network topology. Using this history, we try to route the packets from one node to another using an algorithm that depends on each node’s present available neighbours/contact and the nodes which it has encountered in the recent past. We have also focused on passing the messages to those nodes which are on the move away from the source/forwarder node, as the nodes moving away have a greater probability of disseminating the messages throughout the network and hence increases chances of delivering the message to the destination

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Analysis of current and potential sensor network technologies and their incorporation as embedded structural system

This document provides a brief overview of the actual wireless ad hoc sensor networks technologies and standards available, especially in view of their possible implementation for shipping container protection and monitoring within the framework of the STEC Action aiming at analyzing possible technical solutions to improve the security of the millions of containers moving in and out of Europe. Examples of applications and research projects are reported from the literature to give insights on the possibility of implementation of wireless sensor networks in real world scenarios.JRC.G.5-European laboratory for structural assessmen