One of the major challenges in the use of Radio Frequency-based Identification (RFID) on a large scale is the ability to read a large number of tags quickly. Central to solving this problem is resolving collisions that occur when multiple tags reply to the query of a reader. To this purpose, several MAC protocols for passive RFID systems have been proposed. These typically build on traditional MAC schemes, such as aloha and tree-based protocols. In this paper, we propose a new performance metric by which to judge these anticollision protocols: time system efficiency. This metric provides a direct measure of the time taken to read a group of tags. We then evaluate a set of well-known RFID MAC protocols in light of this metric. Based on the insights gained, we propose a new anticollision protocol, and show that it significantly outperforms previously proposed mechanisms

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In this paper, we address the problem of deploying heterogeneous mobile sensors over a target area. Traditional approaches to mobile sensor deployment are specifically designed for homogeneous networks. Nevertheless, network and device homogeneity is an unrealistic assumption in most practical scenarios, and previous approaches fail when adopted in heterogeneous operative settings. For this reason, we introduce VorLag, a generalization of the Voronoi-based approach which exploits the Laguerre geometry. We theoretically prove the appropriateness of our proposal to the management of heterogeneous networks. In addition, we demonstrate that VorLag can be extended to deal with dynamically generated events or uneven energy depletion due to communications. Finally, by means of simulations, we show that VorLag provides a very stable sensor behavior, with fast and guaranteed termination and moderate energy consumption. We also show that VorLag performs better than its traditional counterpart and other methods based on virtual forces

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Telecommunication networks are evolving from closed systems with limited, standardized services, to open systems which will allow great creativity in building and deploying new services. These systems will heavily leverage Internet technology in an effort to create this open environment. This evolution is being aggressively pursued by Wireless Service Providers (WSPs). Along with the benefits of these networks come increasingly high risks of a variety of attacks that may compromise security. Current, so called second generation (2G) wireless telecommunication networks are implemented using standardized control protocols for user and device authentication, mobility management, session control and services control. These networks are closed in the sense that control messages are exchanged on a private packet-switched network based on the Signaling System No. 7 standards. Because of their closed nature, there are few successful attacks on these networks. The next, so called third generation (3G) wireless telecommunication networks are migrating towards IP technology, with the ultimate goal being an all-IP network. Standards for these systems, called the IP Multimedia Subsystem (IMS) are being defined by the Third Generation Partnership Projects (3GPP and 3GPP2). These networks will use IP for transport of information, and Internet protocols such as the Session Initiation Protocol (SIP) and Mobile IP, for session control and mobility management. These networks open the possibility for IP-based services and must interwork with 2G networks. Because new services will be introduced in the IP-domain of these networks, new attacks on 3G networks are possible. Because IP networks are more accessible than SS7 networks, the control portion of the 3G networks is now more vulnerable to attack. These attacks may be remote denial of service attacks, or attacks that target the integrity of specific services. The means of the attack may vary depending on the interworking model used and the service being offered. In this talk we discuss the different security risks in IP-based 3G networks, different attack types, and the trade-offs of high performance, open network architectures versus secure network infrastructure.

The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowdsensing poses several novel requirements: support for humans-inthe-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowdsourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowdsensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.