IEEE 802.11 is the standard for Wireless Local Area Networks (WLANs) promoted by the Institute of Electrical and Electronics Engineers. Wireless technologies in the LAN environment are becoming increasingly important and the IEEE 802.11 is the most mature technology to date. Previous works have pointed out that the standard protocol can be very inefficient and that an appropriate tuning of its congestion control mechanism (i.e., the backoff algorithm) can drive the IEEE 802.11 protocol close to its optimal behavior. To perform this tuning, a station must have exact knowledge of the network contention level; unfortunately, in a real case, a station cannot have exact knowledge of the network contention level (i.e., number of active stations and length of the message transmitted on the channel), but it, at most, can estimate it. This paper presents and evaluates a distributed mechanism for contention control in IEEE 802.11 Wireless LANs. Our mechanism, named Asymptotically Optimal Backoff (AOB), dynamically adapts the backoff window size to the current network contention level and guarantees that an IEEE 802.11 WLAN asymptotically achieves its optimal channel utilization. The AOB mechanism measures the network contention level by using two simple estimates: the slot utilization and the average size of transmitted frames. These estimates are simple and can be obtained by exploiting information that is already available in the standard protocol. AOB can be used to extend the standard 802.11 access mechanism without requiring any additional hardware. The performance of the IEEE 802.11 protocol, with and without the AOB mechanism, is investigated in the paper through simulation. Simulation results indicate that our mechanism is very effective, robust, and has traffic differentiation potentialities.

The Collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as Smart Cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such IoT scenarios need in order to be pervasive. In our paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, Mobile Crowdsensing (MCS) management for environmental data, information representation and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

No abstract

This paper proposes deployment strategies for consumer Unmanned Aerial Vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are: (i) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. (ii) A distributed game theorybased scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Further, a bioinspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. (iii) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. Our work demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.

Abstract

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smartphones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.

Wireless networks '

No abstract