The wide spread of smart embedded devices and the trend towards their inclusion in a unique Internet of Things are making urgent their full integration within the TCP/IP protocol stack. This paper considers ZigBee, a short-range radio communication standard for embedded devices, and compares two alternative protocol stacks, namely ZigBee Pro and ZigBee IP, developed for the integration of ZigBee in the so called Internet of Things. The performance evaluation is founded on experimental results achieved using two test-beds built to this purpose and is based on service reliability and latency as metrics.

Sensor networks applications often involve the collaboration between sensor nodes, thus requiring a time-synchronization service able to provide a common timescale.

This paper presents a traffic monitoring system implemented through Wireless Sensor Network (WSN) technology within SAFESPOT Project funded by the European Community. The aim of this project is to provide a flexible, robust, low-cost and low-maintenance wireless solution for obtaining traffic-related data that can be used for automatically generating safety warnings at black spots along the road network. The WSN consists of one Gateway Node (GN) and n Sensor Nodes (SNs) deployed along the roadside according to an approximately linear topology. Each SN is able to monitor a section of road, collecting parameters such as vehicle count, speed and direction. Data from the SNs is collected by the GN and delivered to a Road Side Unit (RSU) responsible for fusing it with traffic-related data generated by alternative sources. The system has been tested and performance evaluated under a number of real use-case scenarios.

Instability in packet-switching networks is normally associated with overload conditions, since queueing network models show that, in simple configurations, only overload generates instability. However, some results showing that instability can happen also in underloaded queueing networks began to appear about a decade ago. Underload instabilities can be produced by: 1) customer routes that visit the same queues several times; 2) variations of the customer service times at the different queues; and 3) complex scheduling algorithms. We study, using fluid models and adversarial queueing theory, possible underload instabilities due to flow schedulers in packet networks, focusing on output queued switches with strict priority (SP) schedulers and Generalized Processor Sharing (GPS) schedulers. The considered scenarios always refer to acyclic packet routes and consider customer service times that vary only according to channel capacities, thus resembling the approaches being currently considered to provide QoS in the Internet. Our (in)stability results are rather surprising: SP schedulers appear to be more robust than GPS schedulers whenever exact information on the effective average packet flow rates is not available.

Although instability in packet networks has been traditionally associated with overload conditions (because queueing network models show that, in simple configurations, only overload generates instability), some results showing instability in underloaded packet networks have appeared in the recent literature. In M. Ajmone Marsan, et al. (2003) we studied, with fluid models and with adversarial queueing theory, possible underload instabilities due to complex scheduling algorithms that closely resemble quality of service (QoS) schedulers considered today for packet networks, when sources are non-adaptive. In this paper we extend the study of the underload instabilities to packet networks carrying the traffic generated by elastic (rate-adaptive) sources. In particular, we consider additive-increase, multiplicative-decrease (AIMD) sources, and we show this type of adaptivity is not sufficient to mitigate the phenomena leading to underload instabilities and to reduced network throughput.

Instability in packet-switching networks is normally associated with overload conditions, since queueing network models show that, in simple configurations, only overload generates instability. However, some results showing that instability can happen also in underloaded queueing networks appeared in the recent literature. Underload instabilities can be produced by complex scheduling algorithms, that bear significant resemblance to the Quality of Service (QoS) schedulers considered today for packet networks. In this paper, we study with fluid models and with adversarial queueing theory possible underload instabilities due to strict-priority schedulers and to Generalized Processor Sharing (GPS) schedulers.

No abstract available.