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A survey on facilities for experimental internet of things research

The initial vision of the Internet of Things was of a world in which all physical objects are tagged and uniquely identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions in real-world experimental deployments still hamper their maturation and significant rollout. In this article we identify requirements for the next generation of IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps, and suggest new directions based on experience from recent efforts in this field.

ALBA-R: Load-Balancing Geographic Routing Around Connectivity Holes in Wireless Sensor Networks

This paper presents ALBA-R, a protocol for convergecasting in wireless sensor networks. ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The protocol is localized and distributed, and adapts efficiently to varying traffic and node deployments. Through extensive ns2-based simulations, we show that ALBA-R significantly outperforms other convergecasting protocols and solutions for dealing with connectivity holes, especially in critical traffic conditions and low-density networks. The performance of ALBA-R is also evaluated through experiments in an outdoor testbed of TinyOS motes. Our results show that ALBA-R is an energy-efficient protocol that achieves remarkable performance in terms of packet delivery ratio and end-to-end latency in different scenarios, thus being suitable for real network deployments.

Low-power appliance monitoring using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states.

IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks

This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP.

A detailed simulation study of geographic random forwarding (GeRaF) in wireless sensor networks

This paper provides a thorough performance evaluation of the GeRaF protocol in a multi-hop wireless sensor network scenario. The contributions of the paper are twofold. First we provide an in-depth discussion on the impact of the different protocol parameters and of network features on the protocol performance. Based on the results of the first part of our investigation we design variants of the basic GeRaF protocol which are able to significantly decrease the packet latency and to increase the packet delivery ratio. The study presented in this paper is the first attempt to thoroughly characterize the performance of GeRaF and is a necessary step toward understanding the real protocol behavior before implementing it on real devices

Neighbor Discovery for Opportunistic Networking in Internet of Things Scenarios: A Survey

Neighbor discovery was initially conceived as a means to deal with energy issues at deployment, where the main objective was to acquire information about network topology for subsequent communication. Nevertheless, over recent years, it has been facing new challenges due to the introduction of mobility of nodes over static networks mainly caused by the opportunistic presence of nodes in such a scenario. The focus of discovery has, therefore, shifted toward more challenging environments, where connectivity opportunities need to be exploited for achieving communication. In fact, discovery has traditionally been focused on tradeoffs between energy and latency in order to reach an overlapping of communication times between neighboring nodes. With the introduction of opportunistic networking, neighbor discovery has instead aimed toward the more challenging problem of acquiring knowledge about the patterns of encounters between nodes. Many Internet of Things applications (e.g., smart cities) can, in fact, benefit from such discovery, since end-to-end paths may not directly exist between sources and sinks of data, thus requiring the discovery and exploitation of rare and short connectivity opportunities to relay data. While many of the older discovery approaches are still valid, they are not entirely designed to exploit the properties of these new challenging scenarios. A recent direction in research is, therefore, to learn and exploit knowledge about mobility patterns to improve the efficiency in the discovery process. In this paper, a new classification and taxonomy is presented with an emphasis on recent protocols and advances in this area, summarizing issues and ways for potential improvements. As we will show, knowledge integration in the process of neighbor discovery leads to a more efficient scheduling of the resources when contacts are expected, thus allowing for faster discovery, while, at the same time allowing for energy savings when such contacts are not expected.

ALBA: An Adaptive Load - Balanced Algorithm for Geographic Forwarding in Wireless Sensor Networks

In this paper we propose and analyze ALBA, an original packet forwarding protocol for ad hoc and sensor networks. ALBA follows an integrated approach that combines geographic routing and medium access control (MAC), exploiting the knowledge of node positions in order to achieve energy-efficient data forwarding. The scenario we consider is very critical for medium-high traffic, as contentions for channel access and the resulting collisions lead to performance degradation. To counter this effect, we leverage on network density, favoring the choice relay candidates that are not in overload. With our protocol, nodes strive to channelize traffic toward uncongested network regions, rather than just maximizing the advancement towards the final destination. We carry out extensive simulations that compare ALBA to GeRaF and MACRO, two recently proposed cross-layer approaches with similar goals. The results show that our design achieves very good delivery and latency performance, and can greatly limit energy consumption

Directed diffusion light: low overhead data dissemination in wireless sensor networks

In this paper we introduce directed diffusion light, a variant of the well-known protocol directed diffusion (DD), which results in significant savings in terms of exchanged control messages and energy consumption, and improvements in network lifetime. Directed diffusion light defines local rules to generate a sparse logical topology over which DD can be run. This decreases the costs associated to the required DD periodic flooding. Ns-2 based simulation results show that, when 300 sensor nodes are deployed over a squared area of side 200 m directed diffusion light is able to increase the network lifetime four times, to halve the average energy consumption, and to reduce the control overhead to one third the one of DD.

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.

ROME: Routing Over Mobile Elements in WSNs

In this paper we present ROME, a geographic routing protocol for wireless sensor networks (WSNs) with mobile nodes. ROME design is suited to deal with communication problems in WSN scenarios with high network dynamics, such as nodal addition, nodal removal and node mobility. In addition, it retains desirable properties of protocols for static WSNs such as using cross-layer techniques for performance optimization, dealing with asynchronous nodal duty cycles, and being able to deal with connectivity dead ends. We define the protocol in details and provide detailed simulation-based performance evaluation of ROME. In scenarios with static and mobile nodes together, our ns2-based experiments show that ROME performs remarkably well with respect to metrics such as packet delivery ratio, energy consumption and end-to-end packet latency.