2,803 research outputs found
On Link Estimation in Dense RPL Deployments
The Internet of Things vision foresees billions of
devices to connect the physical world to the digital world. Sensing
applications such as structural health monitoring, surveillance or
smart buildings employ multi-hop wireless networks with high
density to attain sufficient area coverage. Such applications need
networking stacks and routing protocols that can scale with
network size and density while remaining energy-efficient and
lightweight. To this end, the IETF RoLL working group has
designed the IPv6 Routing Protocol for Low-Power and Lossy
Networks (RPL). This paper discusses the problems of link quality
estimation and neighbor management policies when it comes
to handling high densities. We implement and evaluate different
neighbor management policies and link probing techniques in
Contiki’s RPL implementation. We report on our experience
with a 100-node testbed with average 40-degree density. We show
the sensitivity of high density routing with respect to cache sizes
and routing metric initialization. Finally, we devise guidelines for
design and implementation of density-scalable routing protocols
Optimal Relay Selection for Physical-Layer Security in Cooperative Wireless Networks
In this paper, we explore the physical-layer security in cooperative wireless
networks with multiple relays where both amplify-and-forward (AF) and
decode-and-forward (DF) protocols are considered. We propose the AF and DF
based optimal relay selection (i.e., AFbORS and DFbORS) schemes to improve the
wireless security against eavesdropping attack. For the purpose of comparison,
we examine the traditional AFbORS and DFbORS schemes, denoted by T-AFbORS and
TDFbORS, respectively. We also investigate a so-called multiple relay combining
(MRC) framework and present the traditional AF and DF based MRC schemes, called
T-AFbMRC and TDFbMRC, where multiple relays participate in forwarding the
source signal to destination which then combines its received signals from the
multiple relays. We derive closed-form intercept probability expressions of the
proposed AFbORS and DFbORS (i.e., P-AFbORS and P-DFbORS) as well as the
T-AFbORS, TDFbORS, T-AFbMRC and T-DFbMRC schemes in the presence of
eavesdropping attack. We further conduct an asymptotic intercept probability
analysis to evaluate the diversity order performance of relay selection schemes
and show that no matter which relaying protocol is considered (i.e., AF and
DF), the traditional and proposed optimal relay selection approaches both
achieve the diversity order M where M represents the number of relays. In
addition, numerical results show that for both AF and DF protocols, the
intercept probability performance of proposed optimal relay selection is
strictly better than that of the traditional relay selection and multiple relay
combining methods.Comment: 13 page
LPDQ: a self-scheduled TDMA MAC protocol for one-hop dynamic lowpower wireless networks
Current Medium Access Control (MAC) protocols for data collection scenarios with a large number of nodes that generate bursty traffic are based on Low-Power Listening (LPL) for network synchronization and Frame Slotted ALOHA (FSA) as the channel access mechanism. However, FSA has an efficiency bounded to 36.8% due to contention effects, which reduces packet throughput and increases energy consumption. In this paper, we target such scenarios by presenting Low-Power Distributed Queuing (LPDQ), a highly efficient and low-power MAC protocol. LPDQ is able to self-schedule data transmissions, acting as a FSA MAC under light traffic and seamlessly converging to a Time Division Multiple Access (TDMA) MAC under congestion. The paper presents the design principles and the implementation details of LPDQ using low-power commercial radio transceivers. Experiments demonstrate an efficiency close to 99% that is independent of the number of nodes and is fair in terms of resource allocation.Peer ReviewedPostprint (author’s final draft
A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management
Over the last few years, standardisation efforts are consolidating the role of the Routing Protocol for Low-Power and Lossy Networks (RPL) as the standard routing protocol for IPv6-based Wireless Sensor Networks (WSNs). Although many core functionalities are well defined, others are left implementation dependent. Among them, the definition of an efficient link-quality estimation (LQE) strategy is of paramount importance, as it influences significantly both the quality of the selected network routes and nodesâ\u80\u99 energy consumption. In this paper, we present RL-Probe, a novel strategy for link quality monitoring in RPL, which accurately measures link quality with minimal overhead and energy waste. To achieve this goal, RL-Probe leverages both synchronous and asynchronous monitoring schemes to maintain up-to-date information on link quality and to promptly react to sudden topology changes, e.g. due to mobility. Our solution relies on a reinforcement learning model to drive the monitoring procedures in order to minimise the overhead caused by active probing operations. The performance of the proposed solution is assessed by means of simulations and real experiments. Results demonstrated that RL-Probe helps in effectively improving packet loss rates, allowing nodes to promptly react to link quality variations as well as to link failures due to node mobility
Security techniques for sensor systems and the Internet of Things
Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues.
We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in IoT networks, taking into account fixed and variable costs, criticality of different portions of the network, and risk metrics related to a specified security goal.
Monitoring IoT devices and sensors during operation is necessary to detect incidents. We design Kalis, a knowledge-driven intrusion detection technique for IoT that does not target a single protocol or application, and adapts the detection strategy to the network features. As the scale of IoT makes the devices good targets for botnets, we design Heimdall, a whitelist-based anomaly detection technique for detecting and protecting against IoT-based denial of service attacks.
Once our monitoring tools detect an attack, determining its actual cause is crucial to an effective reaction. We design a fine-grained analysis tool for sensor networks that leverages resident packet parameters to determine whether a packet loss attack is node- or link-related and, in the second case, locate the attack source. Moreover, we design a statistical model for determining optimal system thresholds by exploiting packet parameters variances.
With our techniques\u27 diagnosis information, we develop Kinesis, a security incident response system for sensor networks designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in communication and energy overhead
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications
The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime
- …