Jeffrey O. Smith

Online stream processing of machine-to-machine communications traffic: A platform comparison

In a machine-to-machine (M2M) communications system, the deployed devices relay data from on-board sensors to a back-end application over a wireless network. Since the cellular network provides very good coverage (especially in inhabited areas) and is relatively inexpensive, commercial M2M applications often prefer it to other technologies such as WiFi or satellite links. Unfortunately, having been originally designed with human users in mind, the cellular network provides little support to monitor millions of unattended devices. For this reason, it is extremely important to monitor the underlying signalling traffic to detect misbehaving devices or network problems. In the cellular network used by M2M communications systems, the network elements communicate using the Signalling System #7 (SS7), and a real-life system can generate tens of millions of SS7 messages per hour. This paper reports the results of our practical investigation on the possibility to use distributed stream processing systems (DSPSs) to perform real-time analysis of SS7 traffic in a commercial M2M communications system consisting of hundreds of thousands of devices. Through a thorough experimental evaluation based on the analysis of real-world SS7 traces, we present and compare the implementations of a DSPS-based data analysis application on top of either the well-known Storm DSPS or the Quasit middleware. The results show that, by using DSPS services, we are able to largely meet the real-time processing requirements of our use-case scenario.

The University of Texas at Arlington Autonomous Aerial Vehicle—An overview

This paper reports on the status of The University of Texas at Arlington student effort to design, build and fly an Autonomous Aerial Vehicle. Both the 1991 entry into the First International Aerial Robotics Competition as well as refinements being made for 1992 are described. Significant technical highlights include a real-time vision system for target objective tracking, a real-time ultrasonic locator system for position sensing, a novel mechanism for gradually moving from human to computer control, and a hierarchical control structure implemented on a 32-bit microcontroller. Detailed discussion about the design of multivariable automatic controls for stability augmentation is included. Position and attitude control loops are optimized according to a combined ℋ2 and ℋ∞ criteria. We present a modification of a recently published procedure for recovering a desired open-loop transfer function shape within the framework of the mixed ℋ2/ℋ∞ problem. This work has led to a new result that frees a design parameter related to imposing the ℋ∞ constraint. The additional freedom can be used to improve upon the performance and robustness characteristics of the system.

Soft real-time GPRS traffic analytics for commercial M2M communications using spark

Commercial applications of wireless sensor networks, also known as machine-to-machine (M2M) communications, feature hundreds of thousands or even millions of devices. These M2M applications often rely on cellular networks like GSM that were not designed with such use cases in mind. Based on our first-hand experience at a large provider of M2M communications solutions, there is a need for soft real-time traffic analytics solutions to help engineers monitor and manage the millions of devices deployed in these M2M applications. We present a solution for soft real-time GPRS traffic analytics built on Apache Spark, a framework for distributed in-memory computing. The proposed solution captures GPRS traffic, processes it, and decorates it with details about the devices, networks, and M2M applications. It then computes a whole array of statistics that are presented in charts and maps on a live Web application dashboard, or may be fed to other systems for data mining. In a series of experiments, previously captured GPRS traffic from real-life commercial M2M applications is played back to the traffic analytics solution at different rates, and is processed on clusters of varying size. Results show that our solution handles GPRS traffic rates of 3,333 packets/sec, which are 2X the rates of an M2M application with close to one million devices, with a latency below one minute on a Spark cluster with four m1.large slave instances in Amazon EC2 at a cost of

Advancing M2M communications management: A cloud-based system for cellular traffic analysis

7,665/year. These costs can be reduced to approx.

System and Method of On-Shelf Inventory Management

700/year by bidding on SPOT instances.

IP Network Service Redirector Device and Method

Advances in micro-electro-mechanical systems (MEMS), computing hardware, and software algorithms for wireless sensor networks (WSNs) have boosted the adoption of WSNs, also known as machine-to-machine (M2M) communications systems, in many fields, including vehicle tracking, supply chain management, security, and healthcare. Due to the large scale of the deployments of many commercial applications, and the diversity of the hardware and software, the management of these M2M communications systems is becoming more and more cumbersome. Motivated by the challenges posed by a real-life commercial M2M communications system featuring millions of heterogeneous devices connected to hundreds of applications using a GSM cellular network, we developed a cloud-based solution for cellular traffic analysis aimed at M2M communications systems. The proposed system captures and stores all traffic generated by the M2M communications system 24/7, and can process and analyze one day worth of traffic in 2.5 hours for

System and Method for Asset Tracking Using Hybrid WAN/PAN Wireless Technologies

2-3 using cloud computing. We also report on case studies, where the proposed solution was employed to detect misbehaving devices and test different configuration for select devices.