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A Wireless Sensor Network a set of sensor nodes placed in different locations that sense their surroundings and transmit sensed data can have a range of applications related to the environment, healthcare, transportation, security, and other areas. An analysis of published research provides an overview of the ability of mobile elements to improve terrestrial and underwater Wireless Sensor Networks.
As described in the analysis published in the
International Journal of Communication Systems, mobile elements improve communication between sensor nodes by visiting static sensor nodes and collecting their data. This leads to a decrease in energy consumption, improvement in energy efficiency, and extension of the network lifetime.
The
International Journal of Communication Systems provides a forum for R&D, open to researchers from all types of institutions and organisations worldwide, aimed at the increasingly important area of communication technology. The Journal s emphasis is particularly on the issues impacting behaviour at the system, service and management levels. Published twelve times a year, it provides coverage of advances that have a significant potential to impact the immense technical and commercial opportunities in the communications sector. The
International Journal of Communication Systems strives to select a balance of contributions that promotes technical innovation allied to practical relevance across the range of system types and issues.
This paper studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless powered Internet of Things (IoTs) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a Mixed Integer Linear Program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively – both of which exploit AmBC.
Abstract
We consider a Radio Frequency (RF)-charging network where sensor devices harvest energy from a solar-powered Hybrid Access Point (HAP) and transmit their data to the HAP. We aim to optimize the power allocation of both the HAP and devices to maximize their Energy Efficiency (EE), which is defined as the total received data (in bits) for each Joule of consumed energy. Unlike prior works, we consider the case where both the HAP and devices have causal knowledge of channel state information and their energy arrival process. We model the power allocation problem as a Two-layer Markov Decision Process (TMDP), where the first layer corresponds to the HAP and the second layer consists of devices. We then outline a novel, decentralized Q-Learning (QL) solution that employs linear function approximation to represent the large state space. The simulation results show that when the HAP and devices employ our solution, their EE is orders of magnitude higher than competing policies.
Abstract
This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects, and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/di