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Communication Technologies for M2M and IoT Domain
Published in Mohammad Ayoub Khan, Internet of Things, 2022
ANT represents another ultra-low-power, short-range wireless technology designed for sensor networks and similar applications. It, too, operates in the 2.4 GHz ISM band. This protocol is developed and sold by Canadian company Dyna stream Innovations Inc. It defines a wireless communications protocol stack that enables hardware operating in the 2.4 GHz ISM band to communicate by establishing standard rules for co-existence, data representation, authentication, and error detection. It is like Bluetooth low energy but is oriented toward usage with sensors. ANT uses the very short duty cycle technique and deep sleep modes to ensure very low power consumption. The ANT protocol is set up to use a single 1 MHz channel for multiple nodes due to a time division multiplex technique. Each node transmits in its own time slot. Modulation is GFSK. As it shares some aspects with Bluetooth Low Energy, it is comparatively easy to add ANT support to radios already supporting Bluetooth (TEC, 2017 a). Some smartphones in the market natively support ANT protocol.
Connected Bicycles: Potential Research Opportunities in Wireless Sensor Network
Published in Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka, The Internet of Things, 2017
Sadik Kamel Gharghan, Rosdiadee Nordin, Nor Fadzilah Abdullah, Kelechi Anabi
The main dissimilarities between the Bluetooth, ZigBee, and ANT wireless protocols used in bicycle monitoring are summarized in Table 12.1 (Hassan, 2012; Khssibi et al., 2013; Li & Zhuang, 2012; Rault et al., 2014). Bluetooth and ZigBee protocols are based on an IEEE 802 family of standards while ANT is a proprietary standard. Particularly, Classic Bluetooth, ZigBee, and ANT use a low data rate. Bluetooth and ZigBee are designed for a wireless personal area network communication distance of nearly 100 m, whereas ANT has shorter transmission distance of approximately 30 m (Casamassima et al., 2013). The RF-transmitted power level can be modified for all three wireless technologies. As seen in the table, the power consumption is the lowest power for ANT, followed by ZigBee and Bluetooth.
Power Meter Principles for Optimizing Testing, Training and Performance Strategies in Cycling
Published in Youlian Hong, Routledge Handbook of Ergonomics in Sport and Exercise, 2013
Most power meter systems use a standardized area network communication protocol called ANT+. ANT+ is a communication network protocol that is able to collect, transfer and store sensor data used in sports, wellness and home health applications (Dynastream Innovations Inc., 2012). Devices that typically use ANT+ communication protocols include heart rate monitors, speed sensors and small global positioning system (GPS) devices. ANT+ signals transmit over a 2.4 GHz license-free band producing high-quality wireless communication that takes advantage of low-power and low-cost transceivers. Only the Polar Corporation power meter systems do not use ANT+ communication technology. The Polar Corporation uses its own communication technology called a Wireless Integrated Network Device (W.I.N.D.). Unlike ANT+ based systems, which allow various manufacturers to easily transfer data between devices, a W.I.N.D. based system can only use Polar based on-bike monitoring computers, limiting universal data transfer between other manufacturers’ devices. Fortunately, to date, most bicycle computer monitors and power meter systems are ANT+ based, thus allowing cyclists to easily transfer data across manufacturers and various analysis platforms. Also, many new monitors and accessories are incorporating both ANT+ and Bluetooth communication technologies, enhancing their overall compatibilities, including smartphone apps.
Game theory-based dynamic resource allocations scheme in virtual networks
Published in Journal of Information and Telecommunication, 2023
Vianney Kengne Tchendji, Yannick Florian Yankam, Ines Carole Kombou Sihomnou
The resource allocation problem for virtual links can easily be assimilated to a Multi-Commodity Flow problem. Masri et al. (2011) propose an ‘Ant Colony’ meta-heuristic algorithm to solve the problem of routing several multisource and multicast flows, considering the bandwidth and minimizing delay and cost (price). The limitation of this work lies in the fact that the reliability constraint is not studied. To provide more reliability, Wang et al. (2011) and Zhou et al. (2010) proposed a non-cooperative games model where each virtual network tries to maximize its utility function when sharing physical infrastructure. The utility function of the virtual network depends primarily on the provided bandwidth, the physical link state and the price that the virtual network has to pay to share that link. The authors provide an iterative algorithm to achieve Nash's equilibrium1 when sharing bandwidth while considering the performance of each virtual network. The approach aims to dynamically allocate a portion of each physical link's capacity to avoid congestion in the physical infrastructure. The authors fail to consider equity in the bandwidth allocation process and QoS constraints. They also assume that it is the role of the InP to motivate the SP to change its strategy to ensure that the demands of virtual networks will not exceed the link capacity. Without the InP's involvement, the solution does not ensure an efficient and equitable physical link's sharing. This efficiency should depend on the time and income of the SPs to avoid under-utilization (Shun-li et al., 2011).
On the performance analysis of solving the Rubik’s cube using swarm intelligence algorithms
Published in Applied Artificial Intelligence, 2022
The pheromone deposited by an ant , on an edge is given by Equation 8. This means that if an ant travels from node to node then it will deposit some amount of pheromone represented by which is equal to , where is the length of the path between node and node found by the ant . In the context of a Rubik’s cube, the length of the path is the distance from the current state of the cube to the solved state. If an ant applies a move to the cube from state then it will go to the state . The state will have a fitness value, which is the number of moves needed to solve the cube from the state given by Kociemba’s algorithm which is calculated using Equation 2.
A fuzzy irregular cellular automata-based method for the vertex colouring problem
Published in Connection Science, 2020
Mostafa Kashani, Saeid Gorgin, Seyed Vahab Shojaedini
Swarm intelligence is another group of population-based meta-heuristic algorithms which is based on the collective behaviour of decentralised and self-organised systems. These types of methods have been successfully used in a variety of applications, including GC. For example, Costa and Hertz (1997) proposed an ant colony optimisation algorithm for the GC problem RLF and DSATUR. Qin et al. proposed a discrete particle swarm optimisation for the GC problem in which the basic algorithm has been modified in a way to adapt with the discrete nature of the GC problem. In Bui, Nguyen, Patel, and Phan (2008), an ant-based algorithm for GC was introduced in which each ant uses its local information to colour a sub-graph from the main graph. Faraji and Javadi (2011) proposed a GC algorithm based on the behaviour of bees. Their proposed method reported better performance than the ant-based algorithm.