Stephen P. Emmons

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.

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.

Lightweight Internet Traffic Classification: A Subject-Based Solution with Word Embeddings

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.

Evaluating the Optimal Placement of Binary Sensors

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application- level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word- embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of

Local Data Appliance for Collecting and Storing Remote Sensor Data

3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohens Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.