3,194 research outputs found
An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming
The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each nodeās energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal agent-based decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured
Electronically-switched Directional Antennas for Low-power Wireless Networks: A Prototype-driven Evaluation
We study the benefits of electronically-switched directional antennas in low-power wireless networks. This antenna technology may improve energy efficiency by increasing the communication range and by alleviating contention in directions other than the destination, but in principle requires a dedicated network stack. Unlike most existing works, we start by characterizing a real-world antenna prototype, and apply this to an existing low-power wireless stack, which we adapt with minimal changes. Our results show that: i) the combination of a low-cost directional antenna and a conventional network stack already brings significant performance improvements, e.g., nearly halving the radio-on time per delivered packet; ii) the margin of improvement available to alternative clean-slate protocol designs is similarly large and concentrated in the control rather than the data plane; iii) by artificially modifying our antenna's link-layer model, we can point at further potential benefits opened by different antenna designs
Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags
The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals.
To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP
A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks
In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs
Optimal co-design of control, scheduling and routing in multi-hop control networks
A Multi-hop Control Network consists of a plant where the communication
between sensors, actuators and computational units is supported by a (wireless)
multi-hop communication network, and data flow is performed using scheduling
and routing of sensing and actuation data. Given a SISO LTI plant, we will
address the problem of co-designing a digital controller and the network
parameters (scheduling and routing) in order to guarantee stability and
maximize a performance metric on the transient response to a step input, with
constraints on the control effort, on the output overshoot and on the bandwidth
of the communication channel. We show that the above optimization problem is a
polynomial optimization problem, which is generally NP-hard. We provide
sufficient conditions on the network topology, scheduling and routing such that
it is computationally feasible, namely such that it reduces to a convex
optimization problem.Comment: 51st IEEE Conference on Decision and Control, 2012. Accepted for
publication as regular pape
Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems
A cyber-physical system (CPS) employs tight integration of, and
coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS
Joint optimization for wireless sensor networks in critical infrastructures
Energy optimization represents one of the main goals in wireless sensor network design
where a typical sensor node has usually operated by making use of the battery with
limited-capacity. In this thesis, the following main problems are addressed: first, the
joint optimization of the energy consumption and the delay for conventional wireless sensor networks is presented. Second, the joint optimization of the information quality and
energy consumption of the wireless sensor networks based structural health monitoring
is outlined. Finally, the multi-objectives optimization of the former problem under several constraints is shown. In the first main problem, the following points are presented:
we introduce a joint multi-objective optimization formulation for both energy and delay
for most sensor nodes in various applications. Then, we present the Karush-Kuhn-Tucker
analysis to demonstrate the optimal solution for each formulation. We introduce a method
of determining the knee on the Pareto front curve, which meets the network designer interest for focusing on more practical solutions. The sensor node placement optimization has
a significant role in wireless sensor networks, especially in structural health monitoring.
In the second main problem of this work, the existing work optimizes the node placement
and routing separately (by performing routing after carrying out the node placement).
However, this approach does not guarantee the optimality of the overall solution. A joint
optimization of sensor placement, routing, and flow assignment is introduced and is solved
using mixed-integer programming modelling. In the third main problem of this study, we
revisit the placement problem in wireless sensor networks of structural health monitoring by using multi-objective optimization. Furthermore, we take into consideration more
constraints that were not taken into account before. This includes the maximum capacity
per link and the node-disjoint routing. Since maximum capacity constraint is essential
to study the data delivery over limited-capacity wireless links, node-disjoint routing is
necessary to achieve load balancing and longer wireless sensor networks lifetime. We list
the results of the previous problems, and then we evaluate the corresponding results
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