Licensed-assisted access (LAA), which conveys data information via both licensed and unlicensed bands through spectrum aggregation, becomes a promising solution to enhance the capacity of wireless systems. In view of the potential impact on the incumbent system operating in unlicensed bands, the medium access control (MAC) protocol design for LAA system to harmonically coexist with its neighboring incumbent users is one of the most critical and challenging issues. In this paper, we consider a long-term evolution-based LAA (LAA-LTE) system operating in the WiFi unlicensed spectrum, for which the listen-before-talk-based MAC protocol is carefully designed. By quantifying the WiFi throughput and packet delay in the coexisting system, we formulate the constraints of LAA-LTE transmission time to fairly maintain WiFi services. The conditions of known and unknown network size of incumbent WiFi system are each considered separately. Then, the feasible region of LAA-LTE transmission time is determined, and the LAA-LTE protocol is optimized for maximizing the LAA-LTE throughput or the overall throughput contributed by both LAA-LTE and WiFi system. The theoretical analysis is validated via simulation, which also illustrates important observations when LAA-LTE and WiFi systems coexist. This paper offers guidelines to design the LAA-LTE system, paving the way to a controllable, not only harmonious, coexistence of LAA-LTE and WiFi systems in the unlicensed spectrum.

Data gathering is a crucial function of wireless sensor networks (WSNs). In resource-limited WSNs, it is critical to improve cost-efficiency and prolong network lifetime. Sensor networks utilizing deterministic routing paths are particularly vulnerable to attacks. Additionally, repeated use of the same path will introduce load unbalance. In this paper, we propose a cost-aware stochastic compressive data gathering for WSNs. In contrast to traditional compressive sensing (CS) based algorithms that implicitly assume uniform transmission cost, our proposed scheme will consider the cost diversity into the CS data gathering framework. The Markov chain based model will be used to characterize the stochastic data gathering process. An optimization problem is formulated to minimize the total expected cost subjected to the constraints on global degree of randomness and the recovery error. Our proposed algorithm requires less total expected cost to achieve certain level of recovery accuracy. It also prolongs network lifetime due to its load balancing features. Extensive simulations on both synthetic and real data show that the proposed algorithm significantly outperforms benchmark algorithms.

The number of Stationary Battery Systems (SBS) connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile) is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML) based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.

This paper proposes a novel joint clustering and multi-channel medium access control (MAC) protocol for mobile ad hoc networks, based on a scalable two-phase coding scheme. It employs the first-phase codes for differentiating the clusters and the second-phase codes for distinguishing the nodes in a specific cluster. The proposed protocol effectively integrates the procedure of code assignment with dynamic clustering and henceforth substantially reduces the control overhead of code assignment in a code division multiple access (CDMA) based multi-channel MAC protocol while simultaneously combating the hidden terminal problem. Furthermore, the confliction detection and resolution mechanism for the allocation of the first-phase codes as well as the collision avoidance mechanism for the allocation of the second-phase codes in the control channel are also introduced. Analytical framework and extensive simulation results, in terms of control overhead and delay, are provided and compared to the traditional distributed CDMA based multichannel MAC algorithms with or without clustering.

Shrinking water resources all over the world and increasing costs of water consumption have prompted water users and distribution companies to come up with water conserving strategies. We have proposed an energy-efficient smart water monitoring application in [1], using low power RFIDs. In the home environment, there exist many primary interferences within a room, such as cell-phones, Bluetooth devices, TV signals, cordless phones and WiFi devices. In order to reduce the interference from our proposed RFID network for these primary devices, we have proposed a cooperating underlay RFID cognitive network for our smart application on water. These underlay RFIDs should strictly adhere to the interference thresholds to work in parallel with the primary wireless devices [2]. This work is an extension of our previous ventures proposed in [2,3], and we enhanced the previous efforts by introducing a new system model and RFIDs. Our proposed scheme is mutually energy efficient and maximizes the signal-to-noise ratio (SNR) for the RFID link, while keeping the interference levels for the primary network below a certain threshold. A closed form expression for the probability density function (pdf) of the SNR at the destination reader/writer and outage probability are derived. Analytical results are verified through simulations. It is also shown that in comparison to non-cognitive selective cooperation, this scheme performs better in the low SNR region for cognitive networks. Moreover, the hidden Markov model’s (HMM) multi-level variant hierarchical hidden Markov model (HHMM) approach is used for pattern recognition and event detection for the data received for this system [4]. Using this model, a feedback and decision algorithm is also developed. This approach has been applied to simulated water pressure data from RFID motes, which were embedded in metallic water pipes.

This article proposes a partition-based reconfigurable intelligent surface (RIS)-assisted multiuser multiple-input-single-output (MU-MISO) symbiotic radio (SR) system. Unlike conventional RIS designs in SR systems that utilize all elements for secondary transmissions, which restricts its ability to support the primary transmission, the RIS is divided into two subsurfaces: one enhances primary transmissions while the other transmits Internet of Things (IoT) information via spectrum sharing. We formulate a joint optimization problem to minimize transmit power under rate constraints for both primary and IoT transmissions through active beamforming and passive RIS phase-shift design. To tackle the nonconvex constraints and variable coupling in the formulated problem, we propose efficient optimization techniques, including alternating optimization and difference-of-convex methods. Furthermore, we propose a low-complexity interference-free scheme leveraging partitioned RIS to eliminate interuser interference, which is unachievable with conventional RIS design. Simulation results reveal that compared to the conventional scheme, which can be viewed as a special case of partition-based RIS where all the reflecting elements are used to transmit IoT information, partition-based RIS demonstrates superior performance, thereby validating the advantages of RIS partitioning in SR systems.