433 research outputs found
Towards AoI-aware Smart IoT Systems
Age of Information (AoI) has gained importance as a Key Performance Indicator
(KPI) for characterizing the freshness of information in information-update
systems and time-critical applications. Recent theoretical research on the
topic has generated significant understanding of how various algorithms perform
in terms of this metric on various system models and networking scenarios. In
this paper, by the help of the theoretical results, we analyzed the AoI
behavior on real-life networks, using our two test-beds, addressing IoT
networks and regular computers. Excessive number of AoI measurements are
provided for variations of transport protocols such as TCP, UDP and web-socket,
on wired and wireless links. Practical issues such as synchronization and
selection of hardware along with transport protocol, and their effects on AoI
are discussed. The results provide insight toward application and transport
layer mechanisms for optimizing AoI in real-life networks
Semantic Communications in Networked Systems
We present our vision for a departure from the established way of
architecting and assessing communication networks, by incorporating the
semantics of information for communications and control in networked systems.
We define semantics of information, not as the meaning of the messages, but as
their significance, possibly within a real time constraint, relative to the
purpose of the data exchange. We argue that research efforts must focus on
laying the theoretical foundations of a redesign of the entire process of
information generation, transmission and usage in unison by developing:
advanced semantic metrics for communications and control systems; an optimal
sampling theory combining signal sparsity and semantics, for real-time
prediction, reconstruction and control under communication constraints and
delays; semantic compressed sensing techniques for decision making and
inference directly in the compressed domain; semantic-aware data generation,
channel coding, feedback, multiple and random access schemes that reduce the
volume of data and the energy consumption, increasing the number of supportable
devices.Comment: 9 pages, 6 figures, 1500 word
Satisfaction-Aware Data Offloading in Surveillance Systems
In this thesis, exploiting Fully Autonomous Aerial Systems\u27 (FAAS) and Mobile Edge Computing (MEC) servers\u27 computing capabilities to introduce a novel data offloading framework to support the energy and time-efficient video processing in surveillance systems based on satisfaction games. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to support the IP cameras\u27 computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process remotely or locally the data. A non-cooperative game among the cameras is formulated to determine the amount of offloading data to the MEC server and/or the FAAS, and the novel concept of Satisfaction Equilibrium (SE) is introduced where the IP cameras satisfy their minimum QoS prerequisites instead of maximizing their performance by consuming additional system resources. A distributed learning algorithm determines the IP cameras\u27 stable data offloading. Also, a reinforcement learning algorithm indicates the FAAS\u27s movement among the AoIs exploiting the accuracy, timeliness, and certainty of the collected data by the IP cameras per AoI. Detailed numerical and comparative results are presented to show the operation and efficiency of the proposed framework
Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview
The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
Millions of battery-powered sensors deployed for monitoring purposes in a
multitude of scenarios, e.g., agriculture, smart cities, industry, etc.,
require energy-efficient solutions to prolong their lifetime. When these
sensors observe a phenomenon distributed in space and evolving in time, it is
expected that collected observations will be correlated in time and space. In
this paper, we propose a Deep Reinforcement Learning (DRL) based scheduling
mechanism capable of taking advantage of correlated information. We design our
solution using the Deep Deterministic Policy Gradient (DDPG) algorithm. The
proposed mechanism is capable of determining the frequency with which sensors
should transmit their updates, to ensure accurate collection of observations,
while simultaneously considering the energy available. To evaluate our
scheduling mechanism, we use multiple datasets containing environmental
observations obtained in multiple real deployments. The real observations
enable us to model the environment with which the mechanism interacts as
realistically as possible. We show that our solution can significantly extend
the sensors' lifetime. We compare our mechanism to an idealized, all-knowing
scheduler to demonstrate that its performance is near-optimal. Additionally, we
highlight the unique feature of our design, energy-awareness, by displaying the
impact of sensors' energy levels on the frequency of updates
AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead
The increasing pervasive and ubiquitous presence of devices at the edge of
the Internet is creating new scenarios for the emergence of novel services and
applications. This is particularly true for location- and context-aware
services. These services call for new decentralized, self-organizing
communication schemes that are able to face issues related to demanding
resource consumption constraints, while ensuring efficient locality-based
information dissemination and querying. Voronoi-based communication techniques
are among the most widely used solutions in this field. However, when used for
forwarding messages inside closed areas of the network (called Areas of
Interest, AoIs), these solutions generally require a significant overhead in
terms of redundant and/or unnecessary communications. This fact negatively
impacts both the devices' resource consumption levels, as well as the network
bandwidth usage. In order to eliminate all unnecessary communications, in this
paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing
over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward
information within an AoI in a Voronoi network using only local information,
reaching all the devices in the area, and using the lowest possible number of
messages, i.e., just one message for each node included in the AoI. The paper
presents the mathematical and algorithmic descriptions of MABRAVO, as well as
experimental findings of its performance, showing its ability to reduce
communication costs to the strictly minimum required.Comment: Submitted to: IEEE Access; CodeOcean: DOI:10.24433/CO.1722184.v1;
code: https://github.com/michelealbano/mabrav
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
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